# LLM Model Capabilities ## Introduction Large Language Models (LLMs) represent a transformative class of artificial intelligence systems that possess a remarkably broad and versatile range of capabilities extending far beyond simple text generation. These sophisticated neural network architectures, typically built on transformer-based designs and trained on massive datasets containing trillions of tokens, have emerged as general-purpose cognitive tools capable of understanding, processing, generating, and reasoning about information across multiple modalities and domains. Unlike traditional narrowly-focused AI systems designed for specific tasks, modern LLMs demonstrate emergent abilities that enable them to perform hundreds of distinct functions—from fundamental natural language processing tasks like translation and summarization to complex cognitive operations including logical reasoning, code generation, mathematical problem-solving, multimodal understanding, and even orchestrating autonomous agent workflows. The practical significance of these capabilities lies in their ability to serve as flexible building blocks for countless applications: they can be fine-tuned for domain-specific expertise, integrated with external tools and knowledge bases through retrieval-augmented generation (RAG), chained together for multi-step reasoning, and orchestrated as intelligent agents that plan, execute, and adapt to achieve complex goals. This document provides an exhaustive catalog of LLM capabilities organized by functional categories, covering both commonly-used foundational abilities and advanced specialized functions that push the boundaries of what artificial intelligence systems can accomplish in real-world applications. ## Natural Language Processing & Text Generation | **Capability** | **Description** | **Main Usages** | **Advantages** | **Considerations** | **Example** | **Reference** | |----------------|-----------------|-----------------|----------------|-------------------|-------------|---------------| | **Text Generation (Autoregressive)** | Generating coherent, contextually relevant text by predicting and producing the next token sequentially based on preceding context using probabilistic language modeling | • Content creation
• Document drafting
• Email composition
• Article writing
• Response generation | • Human-like text quality
• Maintains coherence across long passages
• Adapts to context and tone
• Scales to various lengths
• No templates required | • Can generate incorrect information
• May lack factual grounding
• Quality depends on prompt
• Token limits constrain length
• Potential for repetitive output | Generate a product launch announcement: "We are thrilled to introduce our revolutionary AI-powered analytics platform that transforms raw data into actionable insights within seconds, empowering businesses to make data-driven decisions with unprecedented speed and accuracy." | 1, 2, 3 | | **Summarization (Abstractive)** | Creating concise, coherent summaries by understanding the core meaning of source text and generating new sentences that capture essential information, rather than simply extracting existing sentences | • Document summarization
• Meeting notes condensation
• Article abstract generation
• Research paper synthesis
• Email thread summaries | • Produces natural, readable summaries
• Captures core meaning effectively
• Handles multiple document formats
• Adjustable compression ratios
• Maintains logical flow | • May miss nuanced details
• Risk of introducing inaccuracies
• Quality varies with input complexity
• Can be computationally expensive
• Requires careful validation | Given a 10-page technical report on climate change impacts, generate a 200-word summary: "Recent studies demonstrate accelerating climate change effects across global ecosystems. Rising temperatures have increased extreme weather frequency by 40% over the past decade..." | 4, 5, 6 | | **Summarization (Extractive)** | Identifying and extracting the most important sentences or passages directly from source text to create summaries while preserving original wording and ensuring high factual fidelity | • News article highlights
• Legal document key points
• Research paper findings extraction
• Quote identification
• Evidence gathering | • Maintains exact source wording
• High factual accuracy
• Preserves technical terminology
• Lower risk of hallucination
• Computationally efficient | • May lack narrative flow
• Can be choppy or disjointed
• Limited compression ratios
• Doesn't generate novel insights
• Redundancy issues | From a 50-page contract, extract key terms: "Section 3.2: Payment terms are Net-30 days. Section 7.1: Either party may terminate with 90 days written notice. Section 12: Liability is limited to $1M aggregate." | 4, 5, 6 | | **Translation (Multilingual)** | Converting text from one natural language to another while preserving meaning, context, tone, and cultural nuances using large-scale multilingual training data | • Document translation
• Real-time conversation translation
• Website localization
• Subtitle generation
• Cross-border communication | • Supports 100+ languages
• Captures contextual meaning
• Handles idioms and cultural references
• Near human-level quality for major languages
• Fast processing speeds | • Quality varies by language pair
• Low-resource languages less accurate
• May miss cultural context
• Technical terminology challenges
• Lacks native fluency review | Translate English to Japanese: "The meeting will be held tomorrow at 3 PM" → "会議は明日午後3時に開催されます" (Kaigi wa ashita gogo 3-ji ni kaisai saremasu) | 7, 8, 9 | | **Paraphrasing** | Restating text using different words and sentence structures while maintaining the original meaning, useful for avoiding plagiarism, improving clarity, or adapting content for different audiences | • Content rewriting
• Plagiarism avoidance
• Clarity improvement
• Audience adaptation
• Variation generation | • Preserves core meaning
• Improves readability
• Multiple variations possible
• Adjustable similarity levels
• Natural language output | • May alter subtle nuances
• Risk of changing meaning
• Quality depends on prompt
• Can introduce verbosity
• Requires validation | Original: "The quick brown fox jumps over the lazy dog." Paraphrased: "A swift auburn canine leaps across a sluggish hound." | 10, 11 | | **Grammar Correction** | Identifying and fixing grammatical errors, punctuation mistakes, spelling errors, and syntax issues while maintaining the original intent and meaning of the text | • Document proofreading
• Email correction
• Essay editing
• Professional communication
• Learning assistance | • Catches multiple error types
• Context-aware corrections
• Maintains writing style
• Real-time feedback
• Explains corrections | • May alter intended meaning
• Style preferences vary
• Over-correction risk
• Limited dialect support
• Requires human review | Input: "Their going to the store tommorow to buy some book." Corrected: "They're going to the store tomorrow to buy some books." | 12, 13 | | **Style Transfer** | Transforming the tone, voice, or stylistic characteristics of text while preserving the core content, enabling adaptation for different audiences, contexts, or communication purposes | • Formal to casual conversion
• Brand voice adaptation
• Academic to layperson translation
• Tone adjustment
• Emotional modulation | • Preserves factual content
• Adjustable style parameters
• Maintains readability
• Multiple style options
• Context-appropriate output | • Subjective style definitions
• May lose nuance
• Consistency challenges
• Cultural variations
• Requires clear target definition | Professional to casual: "We are pleased to inform you that your application has been successful" → "Great news! You got the job!" | 10, 11 | | **Text Expansion** | Elaborating on brief statements or outlines by adding relevant details, examples, explanations, and context to create more comprehensive and developed content | • Outline development
• Bullet point expansion
• Brief to detailed conversion
• Content enrichment
• Idea elaboration | • Adds valuable context
• Maintains core message
• Improves comprehension
• Controllable expansion level
• Natural flow | • Can introduce filler content
• May deviate from intent
• Risk of redundancy
• Length control challenges
• Factual accuracy concerns | Input: "AI is transforming healthcare." Expanded: "Artificial intelligence is revolutionizing the healthcare industry by enabling faster diagnoses through advanced image recognition, personalizing treatment plans based on patient genetic profiles, predicting disease outbreaks using pattern analysis, and automating administrative tasks to reduce clinician burnout." | 10, 11 | | **Text Compression (Conciseness)** | Reducing text length while preserving essential information and core meaning, removing redundancy, verbose language, and non-critical details to create more efficient communication | • Email condensation
• Executive summaries
• Social media adaptation
• Character limit compliance
• Key message extraction | • Removes redundancy
• Preserves critical information
• Improves readability
• Faster comprehension
• Platform optimization | • May lose important details
• Nuance reduction
• Context compression
• Subjectivity in importance
• Over-simplification risk | Original (150 words) compressed to 50: "The quarterly financial results exceeded expectations across all major metrics, with revenue growing 28% year-over-year to $450M, driven primarily by strong enterprise adoption and international expansion." | 10, 11 | ## Knowledge, Reasoning & Information Retrieval | **Capability** | **Description** | **Main Usages** | **Advantages** | **Considerations** | **Example** | **Reference** | |----------------|-----------------|-----------------|----------------|-------------------|-------------|---------------| | **Question Answering** | Providing accurate, relevant responses to natural language questions by comprehending query intent, retrieving relevant knowledge from training data, and formulating coherent answers | • Customer support
• Educational assistance
• Information lookup
• FAQ automation
• Research assistance | • Handles diverse question types
• Natural conversation flow
• Context-aware responses
• Multi-turn capability
• No pre-defined templates | • Knowledge cutoff limitations
• Can generate plausible but incorrect answers
• Source attribution challenges
• Bias in training data
• Requires fact verification | Question: "What are the main causes of inflation?" Answer: "Inflation primarily occurs due to demand-pull factors (increased consumer demand), cost-push factors (rising production costs), built-in inflation (wage-price spirals), and monetary expansion (increased money supply)." | 14, 15, 16 | | **Factual Recall** | Retrieving and articulating specific facts, dates, names, figures, and other discrete pieces of information stored during pre-training on large text corpora | • Trivia answering
• Historical information
• Statistical data retrieval
• Encyclopedia-like queries
• Knowledge verification | • Broad knowledge coverage
• Instant retrieval
• Multi-domain expertise
• Detailed explanations
• Connection of related facts | • Training data cutoff date
• Potential for outdated information
• Hallucination risk
• Cannot update knowledge dynamically
• Source uncertainty | "When was the Eiffel Tower completed?" Answer: "The Eiffel Tower was completed on March 31, 1889, in Paris, France. It was built as the entrance arch for the 1889 World's Fair and was designed by engineer Gustave Eiffel." | 14, 15 | | **Logical Reasoning** | Performing deductive and inductive reasoning by applying logical rules, identifying patterns, drawing conclusions from premises, and solving problems that require structured thinking | • Problem solving
• Argument evaluation
• Hypothesis testing
• Proof construction
• Syllogistic reasoning | • Handles complex logic chains
• Identifies contradictions
• Validates arguments
• Step-by-step reasoning
• Explains logic flow | • Complex proofs challenging
• May skip intermediate steps
• Formal logic limitations
• Can make logical errors
• Requires verification | Given: "All birds have feathers. A penguin is a bird." Conclude: "Therefore, penguins have feathers." The model can identify this as a valid syllogism using modus ponens. | 16, 17, 18 | | **Common Sense Reasoning** | Applying everyday knowledge and intuitions about how the physical and social world works to make inferences and predictions that humans would consider obvious | • Situational understanding
• Implicit knowledge inference
• Real-world problem solving
• Context interpretation
• Plausibility assessment | • Handles unstated assumptions
• Social context understanding
• Physical world knowledge
• Practical problem solving
• Human-like intuitions | • Edge cases challenging
• Cultural variation in "common sense"
• May fail on counterintuitive scenarios
• Lacks embodied experience
• Brittleness in novel situations | Question: "If you drop a glass cup on a concrete floor, what will likely happen?" Answer: "The glass cup will likely shatter or break into pieces because glass is brittle and cannot withstand the impact force when dropped on hard surfaces like concrete." | 16, 17 | | **Pattern Recognition** | Identifying recurring structures, regularities, and relationships in data, text, or sequences that may not be explicitly stated but emerge from systematic analysis | • Trend identification
• Anomaly detection
• Sequence completion
• Relationship discovery
• Predictive analysis | • Discovers hidden patterns
• Multi-modal pattern detection
• Contextual pattern understanding
• Generalization capability
• Complex pattern types | • May identify spurious correlations
• Requires sufficient examples
• Context-dependent accuracy
• Overfitting risk
• Explanation challenges | Given sequence: "2, 4, 8, 16, ..." Model identifies: "Each number is double the previous one, so the next number would be 32 (geometric progression with ratio 2)." | 17, 18 | | **Analogy Making** | Creating comparisons between different concepts or domains by identifying structural similarities, transferring knowledge across contexts, and generating novel analogies for explanation or creativity | • Concept explanation
• Creative thinking
• Teaching and learning
• Metaphor generation
• Cross-domain transfer | • Simplifies complex ideas
• Enhances understanding
• Creative output
• Multiple analogy options
• Cross-domain insights | • May be imprecise
• Cultural relevance varies
• Oversimplification risk
• Misunderstanding potential
• Limited novel combinations | "Explain how a CPU works to a 10-year-old." Answer: "A CPU is like the brain of a computer. Just as your brain tells your body what to do—like when to move your hand or speak—the CPU tells the computer's parts how to work together." | 17 | | **Chain-of-Thought Reasoning** | Breaking down complex problems into sequential intermediate reasoning steps, making the problem-solving process explicit and systematic to improve accuracy on multi-step tasks | • Mathematical problem solving
• Complex reasoning tasks
• Multi-step analysis
• Debugging logic
• Educational explanations | • Improves accuracy significantly
• Transparent reasoning process
• Error identification easier
• Handles complex tasks
• Pedagogically valuable | • Increases token usage
• Slower generation
• May generate incorrect steps
• Requires careful prompting
• Step quality varies | Problem: "A store has 48 apples. They sell half, then receive 20 more. How many now?" Chain: "Step 1: 48 ÷ 2 = 24 (after selling half). Step 2: 24 + 20 = 44. Answer: 44 apples." | 19, 20, 21 | | **Retrieval-Augmented Generation (RAG)** | Enhancing LLM outputs by retrieving relevant information from external knowledge bases or document stores and grounding generated responses in this retrieved factual content | • Enterprise knowledge search
• Document Q&A
• Up-to-date information access
• Domain-specific expertise
• Source attribution | • Access to current information
• Reduces hallucinations
• Source citations possible
• Domain customization
• Scalable knowledge updates | • Retrieval quality critical
• Integration complexity
• Increased latency
• Vector database maintenance
• Chunking strategy importance | User asks: "What was discussed in Q3 board meeting about budget?" RAG system retrieves relevant meeting minutes and generates: "According to the Q3 board minutes (source: board_meeting_2024_Q3.pdf), the board approved a 15% budget increase for R&D initiatives..." | 22, 23, 24, 25 | | **Fact-Checking & Verification** | Assessing the truthfulness and accuracy of claims by comparing them against known facts, identifying inconsistencies, and detecting potential misinformation or hallucinations | • Misinformation detection
• Content verification
• Claim validation
• Hallucination detection
• Quality assurance | • Automated verification
• Identifies contradictions
• Uncertainty quantification
• Multi-source cross-checking
• Real-time validation | • Knowledge limitations
• Requires external sources
• Nuanced claims difficult
• May miss context
• False confidence possible | Claim: "The Earth orbits the Sun in 365 days." Verification: "This statement is approximately correct. The Earth takes about 365.25 days to orbit the Sun, which is why we have leap years every four years to account for the extra quarter day." | 26, 27, 28 | | **Semantic Similarity & Embeddings** | Converting text into dense vector representations in high-dimensional space where semantically similar content is positioned close together, enabling similarity measurement and clustering | • Semantic search
• Document clustering
• Duplicate detection
• Recommendation systems
• Information retrieval | • Captures semantic meaning
• Language-agnostic similarities
• Efficient computation
• Robust to paraphrasing
• Multi-language support | • Dimensionality challenges
• Context window limitations
• Domain adaptation needed
• Interpretation difficulty
• Storage requirements | Query: "best Italian restaurants" matches documents containing "top-rated pizzeria" or "excellent trattorias" even without exact keyword overlap, using cosine similarity between embeddings (similarity score: 0.87). | 29, 30, 31 | ## Programming & Technical Capabilities | **Capability** | **Description** | **Main Usages** | **Advantages** | **Considerations** | **Example** | **Reference** | |----------------|-----------------|-----------------|----------------|-------------------|-------------|---------------| | **Code Generation** | Creating functional source code in various programming languages based on natural language descriptions, specifications, or partial code implementations | • Rapid prototyping
• Boilerplate code creation
• Algorithm implementation
• API integration
• Test case generation | • Accelerates development 2-3x
• Multiple language support
• Best practices application
• Reduces cognitive load
• Learns from context | • Requires code review
• Security vulnerabilities possible
• May use deprecated patterns
• License compliance concerns
• Context window limitations | Prompt: "Create a Python function to calculate Fibonacci sequence recursively with memoization" → Generates optimized code with decorator and cache implementation. | 32, 33, 34 | | **Code Explanation** | Analyzing and describing what existing code does, including logic flow, function purposes, algorithm details, and implementation patterns in human-readable language | • Code documentation
• Onboarding assistance
• Legacy code understanding
• Learning and education
• Code review support | • Simplifies complex code
• Multiple abstraction levels
• Identifies patterns
• Educational value
• Language translation aid | • May miss subtle nuances
• Complex logic challenging
• Context limitations
• Incorrect interpretations possible
• Performance analysis limited | Given a complex SQL query with multiple joins and subqueries, LLM explains: "This query retrieves customer purchase history by joining customers, orders, and products tables, filtering for orders in the last 30 days, and calculating total spend per customer category." | 33, 34 | | **Debugging & Error Detection** | Identifying bugs, logic errors, syntax issues, and performance problems in code, suggesting fixes, and explaining the root causes of errors | • Bug identification
• Error message interpretation
• Logic error detection
• Performance issue spotting
• Security vulnerability detection | • Faster bug resolution
• Explains error causes
• Suggests multiple fixes
• Learns from patterns
• Reduces debugging time | • May miss edge cases
• Complex bugs challenging
• Requires validation
• False positives possible
• Runtime issues harder to detect | Error: "IndexError: list index out of range." LLM identifies: "The loop iterates beyond list length. Change 'for i in range(len(arr)+1)' to 'for i in range(len(arr))' to prevent accessing invalid index." | 33, 35, 36 | | **Code Translation/Transpilation** | Converting code from one programming language to another while preserving functionality, logic, and behavior across different language paradigms | • Legacy code migration
• Cross-platform development
• Language modernization
• Polyglot development
• Proof of concept transfer | • Rapid migration
• Preserves logic
• Multiple language pairs
• Reduces manual effort
• Learns idioms | • Paradigm differences challenging
• Library availability varies
• Performance characteristics differ
• Thorough testing required
• Edge cases may break | Convert Python to JavaScript: `def greet(name): return f"Hello, {name}!"` → `function greet(name) { return \`Hello, ${name}!\`; }` | 33, 37 | | **Documentation Writing** | Generating comprehensive technical documentation including API references, user guides, code comments, README files, and architectural documentation | • API documentation
• README creation
• Inline comments
• User manuals
• Architecture docs | • Consistent documentation style
• Comprehensive coverage
• Multiple format support
• Always up-to-date capability
• Reduces documentation burden | • May lack implementation context
• Requires validation
• Style consistency needed
• Technical depth varies
• Human review essential | Generate API documentation: "Function: calculate_discount(price, percent) - Calculates discounted price. Parameters: price (float): Original price; percent (int): Discount percentage (0-100). Returns: float - Final price after discount. Raises: ValueError if percent not in valid range." | 38 | | **SQL Query Writing (Text-to-SQL)** | Converting natural language questions into executable SQL queries that retrieve or manipulate data from relational databases | • Business intelligence
• Self-service analytics
• Report generation
• Data exploration
• Ad-hoc queries | • Democratizes data access
• No SQL knowledge required
• Handles complex joins
• Reduces analyst workload
• Faster insights | • Schema complexity limits accuracy
• Requires metadata understanding
• Ambiguous queries problematic
• Validation needed
• Security risks (injection) | Question: "Show top 10 customers by revenue in Q4 2024" → SQL: `SELECT customer_name, SUM(order_total) as revenue FROM customers JOIN orders ON customers.id=orders.customer_id WHERE order_date BETWEEN '2024-10-01' AND '2024-12-31' GROUP BY customer_name ORDER BY revenue DESC LIMIT 10;` | 39, 40 | | **Test Case Generation** | Creating unit tests, integration tests, and test scenarios that verify code correctness, edge cases, and expected behavior across different inputs | • Automated testing
• Edge case discovery
• Test-driven development
• Regression testing
• Quality assurance | • Comprehensive test coverage
• Identifies edge cases
• Multiple testing frameworks
• Reduces manual test writing
• Improves code quality | • May miss subtle cases
• Logic understanding needed
• Maintenance overhead
• False sense of security
• Integration testing challenges | For function `def divide(a, b): return a/b`, generate tests: "test_normal_division(), test_division_by_zero(), test_negative_numbers(), test_float_precision(), test_large_numbers(), test_divide_by_one(), test_zero_divided_by_number()" | 33, 34 | | **Refactoring Suggestions** | Recommending code improvements including design pattern applications, performance optimizations, readability enhancements, and architectural restructuring | • Code quality improvement
• Performance optimization
• Design pattern application
• Technical debt reduction
• Maintainability enhancement | • Identifies improvement opportunities
• Applies best practices
• Multiple suggestions
• Explains rationale
• Language-specific idioms | • Context limitations
• May suggest breaking changes
• Trade-off evaluation needed
• Testing requirements
• Subjective recommendations | Identifies code smell: "This function has 15 parameters (too many). Refactor suggestion: Create a configuration object to group related parameters. Benefits: Improved readability, easier testing, better extensibility." | 33 | ## Analysis, Classification & Information Extraction | **Capability** | **Description** | **Main Usages** | **Advantages** | **Considerations** | **Example** | **Reference** | |----------------|-----------------|-----------------|----------------|-------------------|-------------|---------------| | **Sentiment Analysis** | Determining the emotional tone, opinion polarity (positive, negative, neutral), and subjective attitudes expressed in text across various granularities | • Social media monitoring
• Customer feedback analysis
• Brand reputation tracking
• Review aggregation
• Market research | • Multi-level granularity
• Contextual understanding
• Emotion detection
• Real-time processing
• Multi-language support | • Sarcasm detection challenges
• Context-dependent accuracy
• Cultural nuances
• Ambiguous expressions
• Balanced texts difficult | Review: "The product works okay, but customer service was terrible." Analysis: Overall sentiment: Negative (0.3/1.0), Product sentiment: Neutral (0.5), Service sentiment: Negative (0.1), Key emotions: Disappointment, frustration. | 41, 42 | | **Text Classification** | Assigning predefined categorical labels to text documents based on content, topic, intent, or other attributes through supervised or few-shot learning | • Email routing
• Content moderation
• Document organization
• Spam detection
• Intent recognition | • Multi-class classification
• Few-shot capability
• High accuracy
• Hierarchical categories
• Confidence scores | • Requires clear categories
• Overlapping classes challenging
• Edge cases difficult
• Bias in classifications
• New categories need retraining | Classify customer inquiry: "I can't log into my account and forgot my password" → Categories: [Technical Support], [Account Issues], [Authentication], Priority: High, Intent: Password Reset | 41, 43, 44 | | **Named Entity Recognition (NER)** | Identifying and classifying named entities in text such as persons, organizations, locations, dates, quantities, and custom domain-specific entities | • Information extraction
• Knowledge graph construction
• Document indexing
• Data anonymization
• Relation extraction | • Multi-entity type support
• Context-aware recognition
• Handles ambiguity
• Custom entity types
• Nested entities | • Entity boundary detection
• Ambiguous names challenging
• Domain adaptation needed
• Rare entities missed
• Requires fine-tuning for domains | Text: "Apple CEO Tim Cook announced new products in Cupertino on March 15th." Entities: [Organization: Apple], [Person: Tim Cook], [Location: Cupertino], [Date: March 15th], [Event: product announcement] | 41, 43, 44, 45 | | **Key Phrase Extraction** | Identifying and extracting the most important terms, concepts, and multi-word phrases that represent the core topics or themes in a document | • Document summarization
• SEO optimization
• Metadata generation
• Search indexing
• Topic identification | • Automatic keyword discovery
• Multi-word phrase support
• Importance ranking
• Context-aware selection
• No predefined vocabulary needed | • Domain-specific terminology
• Subjectivity in importance
• Redundancy handling
• Length vs. informativeness trade-off
• Coherence challenges | Text about climate change extracts key phrases: "global warming" (0.95), "carbon emissions" (0.89), "renewable energy" (0.84), "temperature rise" (0.78), "greenhouse gases" (0.75) [ranked by importance score] | 41 | | **Topic Modeling** | Discovering abstract topics or themes that occur across a collection of documents by identifying patterns in word co-occurrence and semantic relationships | • Content organization
• Trend analysis
• Document clustering
• Recommendation systems
• Research discovery | • Unsupervised discovery
• Multiple topics per document
• Hierarchical topics
• Temporal evolution tracking
• Cross-document patterns | • Topic interpretation needed
• Number of topics selection
• Computational complexity
• Coherence varies
• Dynamic topics challenging | Analyzing 1000 customer reviews identifies topics: Topic 1 (35%): product quality, durability, materials; Topic 2 (28%): customer service, support, response time; Topic 3 (22%): pricing, value, affordability; Topic 4 (15%): delivery, shipping, packaging. | 41 | | **Information Extraction** | Identifying and extracting structured information from unstructured text, including entities, relationships, events, and attributes to populate databases or knowledge bases | • Database population
• Knowledge graph construction
• Contract analysis
• Form filling
• Data mining | • Converts unstructured to structured
• Relationship detection
• Multi-field extraction
• Template-free approach
• Domain adaptable | • Complex relationships challenging
• Accuracy depends on text quality
• Requires validation
• Schema definition needed
• Ambiguity handling | From job posting extract: {title: "Senior Data Scientist", location: "San Francisco, CA", salary_range: "$150K-$200K", requirements: ["PhD in CS/Stats", "5+ years experience", "Python, R"], company: "TechCorp"} | 44, 45, 46 | | **Relation Extraction** | Identifying and classifying semantic relationships between entities mentioned in text, such as "works_for", "located_in", "causes", or custom relations | • Knowledge graph building
• Semantic search
• Question answering
• Information discovery
• Network analysis | • Complex relationship types
• Multiple relations per sentence
• Directional relationships
• Confidence scoring
• Context-aware | • Implicit relations difficult
• Distant supervision challenges
• Domain-specific relations
• Ambiguity handling
• Requires extensive training | Text: "Microsoft acquired LinkedIn for $26.2 billion in 2016." Relations: [Microsoft, acquired, LinkedIn], [acquisition_price, $26.2 billion], [acquisition_date, 2016], [acquirer, Microsoft], [acquired, LinkedIn] | 44, 45 | | **Intent Recognition** | Determining the underlying goal, purpose, or desired action that a user expresses in their input, crucial for conversational AI and task automation | • Chatbot routing
• Voice assistants
• Command interpretation
• Task automation
• User interface control | • Multi-intent detection
• Context-aware recognition
• Confidence scoring
• Handles ambiguity
• Real-time processing | • Ambiguous intents challenging
• Context dependency
• New intents need examples
• Edge cases difficult
• May require clarification | User: "I want to check my account balance and transfer $500 to savings." Intents: [primary: check_balance (0.95), secondary: transfer_money (0.92)], Entities: [amount: $500, destination: savings_account] | 41, 43 | ## Creative Tasks & Content Generation | **Capability** | **Description** | **Main Usages** | **Advantages** | **Considerations** | **Example** | **Reference** | |----------------|-----------------|-----------------|----------------|-------------------|-------------|---------------| | **Story Writing** | Creating engaging narratives with characters, plot development, conflict resolution, and thematic elements across various genres and lengths | • Fiction writing
• Screenplay drafting
• Novel chapters
• Short stories
• Interactive narratives | • Consistent narrative voice
• Genre adaptation
• Character development
• Plot coherence
• Creative variations | • May lack deep originality
• Character consistency challenges
• Emotional depth limitations
• Long-form coherence difficult
• Requires human editing | Generate a mystery story opening: "Detective Sarah Chen stood in the rain-soaked alley, her flashlight beam cutting through the darkness to reveal the impossible: a locked room, no signs of forced entry, and a victim who had been dead for hours—yet whose phone showed a text sent just ten minutes ago." | 47, 48, 49 | | **Poetry Composition** | Creating poetry in various forms including sonnets, haikus, free verse, rhyming couplets, with attention to rhythm, meter, imagery, and figurative language | • Poem generation
• Lyric writing
• Greeting cards
• Creative expression
• Educational examples | • Multiple poetic forms
• Meter and rhyme control
• Imagery and metaphor
• Style adaptation
• Theme exploration | • May lack genuine emotion
• Originality questions
• Deep meaning challenging
• Cultural context limitations
• Formulaic tendencies | Haiku about autumn: "Crimson leaves descend
through misty morning silence—
earth's quilted blanket." | 47, 48 | | **Script Writing (Dialogue)** | Crafting dialogue, stage directions, and scene descriptions for theatrical productions, films, television, or other dramatic media | • Screenplay writing
• TV show scripts
• Video game dialogue
• Podcast scripts
• Training videos | • Natural dialogue flow
• Character voice distinction
• Scene structure
• Format compliance
• Pacing control | • Character depth limitations
• Subtext challenges
• Visual storytelling aspects
• Industry standards knowledge
• Lacks directorial vision | Dialogue scene: **INT. COFFEE SHOP - MORNING**
ALEX: (nervously stirring coffee) So, about that project deadline...
JORDAN: (without looking up) It was yesterday, Alex.
ALEX: (wincing) I was afraid you'd say that. | 47, 49 | | **Idea Brainstorming** | Generating diverse, creative ideas, solutions, or approaches to problems, challenges, or creative projects through associative thinking and novel combinations | • Product ideation
• Marketing campaigns
• Problem solving
• Research directions
• Innovation workshops | • High volume idea generation
• Diverse perspectives
• Cross-domain connections
• Rapid iteration
• Removes creative blocks | • Quality varies widely
• Feasibility not guaranteed
• May be too generic
• Requires filtering
• Lacks deep domain expertise | Brainstorm mobile app ideas for busy parents: 1) Family calendar with AI scheduling, 2) Meal planning with dietary restrictions, 3) Carpool coordination platform, 4) Homework help marketplace, 5) Screen time gamification system, 6) Emergency contact network, 7) Shared grocery lists with store navigation, 8) Kids' milestone tracking with photo timeline... | 47, 50 | | **Content Repurposing** | Transforming existing content into different formats, styles, or media types while preserving core messages and adapting for new audiences or platforms | • Blog to social media
• Long-form to snippets
• Technical to layperson
• Video scripts to articles
• Cross-platform adaptation | • Multi-format output
• Audience adaptation
• Time efficiency
• Consistent messaging
• Platform optimization | • May lose nuance
• Format constraints
• Context adaptation challenges
• Voice consistency
• Requires content understanding | Convert blog post to Twitter thread: "Blog (2000 words) → Thread: 1/ Big news in AI! Here's what the latest breakthrough means for you... 2/ Traditional models struggled with X, but this new approach... 3/ Real-world impact: imagine your... [8 tweets total]" | 47 | | **Character Creation** | Developing detailed fictional characters with backgrounds, personalities, motivations, conflicts, and character arcs for stories, games, or role-playing | • Fiction writing
• Game development
• Role-playing games
• Screenplay development
• Interactive narratives | • Detailed profiles
• Psychological depth
• Consistent traits
• Backstory generation
• Multiple archetypes | • May be stereotypical
• Complexity limitations
• Original voices challenging
• Growth arcs formulaic
• Lacks lived experience | Character profile: "Dr. Elena Vasquez, 42, forensic psychologist haunted by an unsolved case from her residency. Strengths: exceptional pattern recognition, empathy. Flaws: obsessive tendencies, insomnia. Motivation: redemption through solving cold cases. Conflict: career success vs. personal relationships." | 47, 49 | | **Slogan & Tagline Generation** | Creating memorable, concise phrases that capture brand essence, product benefits, or campaign messages with impact and creativity | • Brand identity
• Marketing campaigns
• Product launches
• Advertising
• Event promotion | • Multiple variations quickly
• Memorable phrasing
• Brand alignment
• Tone adaptation
• A/B testing options | • May lack originality
• Trademark conflicts possible
• Cultural sensitivity needed
• Subjective evaluation
• Requires human selection | For eco-friendly cleaning products: "Pure Clean, Pure Planet" | "Where Nature Meets Sparkle" | "Clean Conscience, Cleaner Home" | "Dirt's Worst Enemy, Earth's Best Friend" | 47 | | **World Building** | Creating comprehensive fictional settings including geography, history, cultures, social structures, and rules that govern imaginary worlds | • Fantasy novels
• Sci-fi universes
• Game design
• Tabletop RPGs
• Worldbuilding projects | • Comprehensive details
• Consistent internal logic
• Multiple dimensions
• Interconnected systems
• Evolution over time | • Consistency challenges
• Overwhelming detail possible
• May lack uniqueness
• Integration complexity
• Requires extensive prompting | Create a fantasy world: "Aethermoor: A continent of floating islands connected by ancient crystal bridges. Magic flows from the Great Spire at the center. Three kingdoms: Skyreach (air mages), Deepvale (earth crafters), Misthollow (water healers). Currency: Aether shards. Government: Tri-Council. Conflict: Crystal bridges weakening, threatening isolation..." | 47, 48 | ## Structured Output Generation & Formatting | **Capability** | **Description** | **Main Usages** | **Advantages** | **Considerations** | **Example** | **Reference** | |----------------|-----------------|-----------------|----------------|-------------------|-------------|---------------| | **JSON Generation** | Creating valid, well-structured JSON documents following specified schemas with proper syntax, data types, and hierarchical organization | • API responses
• Configuration files
• Data exchange
• Structured logging
• Database records | • Schema compliance
• Type safety
• Nested structures
• Validation ready
• Machine-readable | • Schema complexity limitations
• Validation required
• May deviate from schema
• Large objects challenging
• Escaping issues | Generate user profile JSON: `{"user_id": 12345, "name": "Jane Doe", "email": "jane@example.com", "preferences": {"theme": "dark", "notifications": true}, "created": "2024-01-15T10:30:00Z"}` | 51, 52, 53 | | **XML/YAML Generation** | Producing properly formatted XML or YAML documents with correct syntax, indentation, attributes, and hierarchical structure for configuration and data representation | • Configuration management
• Data serialization
• Document markup
• API specifications
• CI/CD pipelines | • Human-readable formats
• Complex structures
• Comment support
• Standards compliance
• Tool integration | • Indentation sensitivity (YAML)
• Verbosity (XML)
• Escaping complexity
• Version compatibility
• Validation needed | YAML config: `database:\n host: localhost\n port: 5432\n credentials:\n user: admin\n password: secret\n pool_size: 20` | 51, 54 | | **Table Creation** | Organizing information into structured tables with rows, columns, headers, and proper formatting in various output formats (Markdown, HTML, LaTeX) | • Data presentation
• Report generation
• Documentation
• Comparison charts
• Spreadsheet templates | • Clear data organization
• Multiple format support
• Customizable columns
• Sortable presentation
• Visual clarity | • Complex relationships difficult
• Formatting limitations
• Large tables unwieldy
• Layout constraints
• Nested structures challenging | \| Product \| Price \| Stock \|
\|---------|-------|-------\|
\| Laptop \| $899 \| 15 \|
\| Mouse \| $29 \| 157 \|
\| Keyboard \| $79 \| 43 \| | 51, 54 | | **List Generation** | Creating organized lists (bulleted, numbered, nested) with appropriate structure, hierarchy, and logical ordering for various content types | • Task lists
• Feature enumeration
• Step-by-step instructions
• Pros/cons lists
• Hierarchical outlines | • Clear organization
• Easy scanning
• Hierarchy support
• Multiple formats
• Logical ordering | • Over-nesting issues
• Ordering ambiguity
• Incomplete lists possible
• Balance challenges
• Context dependency | Project tasks: 1) Planning Phase: a) Define requirements, b) Create timeline; 2) Development: a) Backend API, b) Frontend UI, c) Testing; 3) Deployment: a) Staging, b) Production | 51, 54 | | **Outline Creation** | Developing hierarchical structures that organize ideas, arguments, or content into logical sections with main points and supporting details | • Document planning
• Presentation structure
• Research organization
• Book chapters
• Course curriculum | • Logical flow
• Hierarchical organization
• Comprehensive coverage
• Easy restructuring
• Planning efficiency | • May miss connections
• Depth vs. breadth balance
• Reorganization needs
• Subjective structure
• Detail level varies | Outline for business proposal: I. Executive Summary; II. Problem Statement: A. Market gap, B. Customer pain points; III. Proposed Solution: A. Product features, B. Implementation; IV. Financial Projections; V. Conclusion | 54 | | **Form Filling** | Extracting relevant information from context and populating structured forms with appropriate field values, ensuring data type and format compliance | • Application automation
• Data entry
• Document processing
• CRM updates
• Survey completion | • Automated data population
• Accuracy improvements
• Format compliance
• Speed gains
• Error reduction | • Ambiguous fields challenging
• Validation requirements
• Missing information handling
• Format variations
• Context interpretation | From email: "John Smith, john@email.com, interested in premium plan" → Form: {First: John, Last: Smith, Email: john@email.com, Interest: Premium Plan, Source: Email} | 51, 54 | | **Template Filling** | Populating predefined templates with dynamic content while maintaining consistent structure, formatting, and style across multiple instances | • Email templates
• Report generation
• Document automation
• Marketing materials
• Contract creation | • Consistency guaranteed
• Rapid generation
• Brand compliance
• Multiple instances
• Customization supported | • Template design needed
• Flexibility limitations
• Context adaptation
• Placeholder management
• Version control | Email template: "Dear {name}, Thank you for your interest in {product}. Based on your needs for {requirement}, we recommend {solution}. Our team will contact you within {timeframe}." | 51, 54 | | **Grammar-Based Constrained Generation** | Generating text that strictly adheres to formal grammar rules, regular expressions, or custom constraints beyond simple JSON schemas | • Domain-specific languages
• Configuration syntax
• Mathematical expressions
• Programming language snippets
• Structured formats | • Strict format compliance
• Complex constraint support
• Validation guaranteed
• Custom grammar support
• Syntax correctness | • Complex grammar definition
• Reduced flexibility
• Generation speed impact
• Limited creativity
• Debugging difficulty | Generate valid regex for email: `^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$` (guaranteed syntactically valid) | 51, 52 | ## Conversational & Interactive Capabilities | **Capability** | **Description** | **Main Usages** | **Advantages** | **Considerations** | **Example** | **Reference** | |----------------|-----------------|-----------------|----------------|-------------------|-------------|---------------| | **Multi-Turn Dialogue** | Maintaining coherent, contextually relevant conversations across multiple exchanges while tracking topics, references, and conversation state | • Customer service chatbots
• Virtual assistants
• Interview simulation
• Tutoring systems
• Companionship AI | • Natural conversation flow
• Context retention
• Topic transitions
• Long-context handling
• Human-like interactions | • Context window limits
• Memory degradation
• Topic drift
• Inconsistency over time
• Computational costs | User: "I need help with my order"
Agent: "I'd be happy to help. What's your order number?"
User: "It's 12345"
Agent: "Thanks. I see your order shipped yesterday. Is there an issue?"
User: "When will it arrive?"
Agent: "Based on tracking, it should arrive by Friday." | 55, 56, 57 | | **Context Maintenance** | Preserving and utilizing information from earlier in the conversation or document to ensure coherent, contextually appropriate responses | • Long conversations
• Document analysis
• Task completion
• Information recall
• Relationship tracking | • Information persistence
• Pronoun resolution
• Implicit reference understanding
• Temporal awareness
• Cross-turn coherence | • Long-range dependencies
• Context window limits
• Information prioritization
• Outdated context risks
• Memory mechanisms needed | Turn 1: "Tell me about climate change"
[Detailed response]
Turn 5: "What were those main causes you mentioned earlier?"
Model recalls: "Earlier I mentioned greenhouse gases, deforestation, and industrial emissions as primary causes..." | 55, 57, 58 | | **Persona Adoption** | Assuming specific character roles, personalities, communication styles, or expert identities to provide tailored, role-appropriate responses | • Role-playing
• Character simulation
• Expert consultation
• Brand voice
• Training scenarios | • Consistent personality
• Role-appropriate responses
• Enhanced engagement
• Multiple personas
• Domain expertise simulation | • Persona drift over time
• May conflict with safety
• Authenticity questions
• Stereotype risks
• Ethical boundaries | System: "You are a friendly, patient math tutor for 10-year-olds."
Response style: "Great question! Let's break this down together. Think of multiplication like having groups of things..." [Uses simple language, encouraging tone, analogies] | 55, 56, 59 | | **Question Clarification** | Requesting additional information or clarification when faced with ambiguous, incomplete, or unclear user inputs before attempting to answer | • Disambiguation
• Requirements gathering
• Intent refinement
• Error prevention
• Information completeness | • Prevents incorrect assumptions
• Improves accuracy
• User-guided responses
• Error reduction
• Better user experience | • May frustrate users
• Increases interaction length
• Over-clarification possible
• Balance needed
• Context judgment required | User: "Tell me about Apple"
Agent: "I'd be happy to help! Are you asking about: 1) Apple Inc. (the technology company), 2) Apples (the fruit), or 3) Something else?" | 55, 57 | | **Empathetic Response** | Recognizing and appropriately responding to emotional content in user inputs with sensitivity, understanding, and contextually appropriate supportive language | • Mental health support
• Customer complaints
• Grief counseling
• Healthcare communication
• Crisis intervention | • Emotional intelligence
• Supportive language
• Reduced user frustration
• Trust building
• Appropriate tone | • Not a replacement for professionals
• Misreading emotions possible
• Cultural differences
• Ethical boundaries
• Safety protocols needed | User: "I've been feeling really overwhelmed lately and nothing seems to work."
Response: "I hear that you're going through a really difficult time, and I'm sorry you're feeling this way. It's completely understandable to feel overwhelmed. Would you like to talk about what's been weighing on you, or would you prefer some resources for support?" | 55, 56 | | **Conversational Memory (Session-Based)** | Maintaining information across a single conversation session, including user preferences, stated facts, and interaction history for coherent dialogue | • Chatbot interactions
• Task-oriented dialogue
• Personalized assistance
• Information gathering
• Progressive disclosure | • Session coherence
• Reduced repetition
• Progressive refinement
• Preference learning
• Efficient interaction | • Session boundaries
• Information persistence limits
• Cross-session memory lacking
• Privacy considerations
• Reset mechanisms needed | Session: User states "I prefer vegetarian options" early on. Later: "Recommend a restaurant" → Model remembers preference and suggests vegetarian restaurants without re-asking. | 55, 57, 58 | | **Conversational Memory (Long-Term)** | Retaining and utilizing information across multiple sessions and extended time periods using external memory systems or retrieval mechanisms | • Personalized assistants
• Customer relationship management
• Healthcare continuity
• Education tracking
• Long-term projects | • True personalization
• Relationship building
• Progress tracking
• Adaptive learning
• Continuity of care | • Privacy and security
• Storage requirements
• Memory accuracy decay
• Update mechanisms needed
• Data governance | User returns after weeks: "Remember that project we discussed?"
Agent: "Yes, you were working on the mobile app for fitness tracking. Last time you mentioned you were struggling with the notification system. How's that progressing?" | 55, 58 | | **Proactive Suggestions** | Anticipating user needs and offering relevant suggestions, recommendations, or next actions without explicit requests based on context and patterns | • Virtual assistants
• Workflow optimization
• Productivity tools
• E-commerce
• Content discovery | • Enhanced user experience
• Efficiency gains
• Discovery of features
• Anticipatory service
• Guided interactions | • May be intrusive
• Accuracy of predictions
• Timing challenges
• User preference variations
• Over-suggestion fatigue | User schedules meeting for 2 PM.
Agent proactively suggests: "I notice you have a meeting at 2 PM across town. Would you like me to set a reminder to leave by 1:30 PM to account for traffic?" | 55 | ## Mathematical & Analytical Capabilities | **Capability** | **Description** | **Main Usages** | **Advantages** | **Considerations** | **Example** | **Reference** | |----------------|-----------------|-----------------|----------------|-------------------|-------------|---------------| | **Basic Arithmetic** | Performing fundamental mathematical operations including addition, subtraction, multiplication, division, and exponentiation on numbers | • Calculations
• Financial computations
• Data analysis
• Quantity estimations
• Unit conversions | • Instant calculation
• Multiple operations
• Large number handling
• Precision control
• Step-by-step explanation | • Floating-point precision issues
• May make calculation errors
• Better to use code for accuracy
• Rounding inconsistencies
• Complex expressions challenging | Calculate: "If a product costs $49.99 and is discounted by 25%, what's the final price?" Answer: "$49.99 × 0.75 = $37.49" (Better: Generate code for precision) | 60, 61, 62 | | **Equation Solving** | Solving mathematical equations including algebraic, polynomial, differential, and system of equations through symbolic manipulation or numerical methods | • Homework assistance
• Engineering calculations
• Scientific modeling
• Problem solving
• Formula derivation | • Multiple equation types
• Step-by-step solutions
• Verification possible
• Explanation provided
• Multiple solution methods | • Complex equations challenging
• Numerical accuracy issues
• May skip steps
• Limited symbolic computation
• Better to use specialized tools | Solve: "2x + 5 = 13" → "2x = 13 - 5 → 2x = 8 → x = 4" OR differential equation: "dy/dx = 2x" → "y = x² + C" | 60, 61, 62 | | **Data Interpretation** | Analyzing numerical data, identifying trends, calculating statistics, and drawing insights from datasets, tables, or visualizations | • Business analytics
• Research analysis
• Report generation
• Trend identification
• Statistical summaries | • Pattern recognition
• Multiple analysis types
• Natural language insights
• Visualization interpretation
• Context-aware analysis | • Limited computational precision
• May miss subtle patterns
• Statistical rigor varies
• Causation vs correlation confusion
• Requires data quality | Given sales data: Q1: $450K, Q2: $520K, Q3: $485K, Q4: $610K → "Sales show 35.6% YoY growth with strong Q4 performance (+25.8% vs Q3). Notable seasonality with Q4 peak. Average quarterly: $516K." | 60, 62, 63 | | **Statistical Explanation** | Describing statistical concepts, interpreting statistical results, explaining significance tests, and contextualizing statistical measures | • Research interpretation
• Results explanation
• Educational assistance
• Report writing
• Data literacy | • Accessible explanations
• Multiple difficulty levels
• Context-aware descriptions
• Example provision
• Assumption clarification | • May oversimplify
• Nuance loss possible
• Requires domain knowledge
• Misinterpretation risks
• Not a replacement for statisticians | Explain p-value: "A p-value of 0.03 means there's only a 3% probability that the observed result occurred by random chance if the null hypothesis were true. Since this is below the 0.05 threshold, we reject the null hypothesis, suggesting the effect is statistically significant." | 62, 63 | | **Formula Application** | Applying mathematical and scientific formulas to solve real-world problems with correct variable substitution, unit handling, and result interpretation | • Physics calculations
• Engineering problems
• Financial modeling
• Chemistry computations
• Unit conversions | • Wide formula knowledge
• Unit awareness
• Step-by-step application
• Error checking
• Result interpretation | • Formula selection errors
• Unit conversion mistakes
• Assumption violations
• Precision limitations
• Domain expertise needed | Apply compound interest: "Principal: $10,000, Rate: 5% annual, Time: 3 years, Compounded quarterly." Formula: A = P(1 + r/n)^(nt) → A = 10000(1 + 0.05/4)^(4×3) = $11,160.54 | 60, 62 | | **Word Problem Solving** | Converting natural language mathematical word problems into formal mathematical representations and solving them with explanations | • Homework help
• Educational assessment
• Application problems
• Real-world scenarios
• Logic puzzles | • Natural language understanding
• Problem decomposition
• Multiple solution paths
• Explanation generation
• Error identification | • Complex problems challenging
• Ambiguity handling
• Implicit assumptions
• Multi-step complexity
• Verification needed | "A train travels 120 miles in 2 hours. How fast is it going?" → "Speed = Distance ÷ Time = 120 miles ÷ 2 hours = 60 miles per hour" | 60, 61, 62 | | **Mathematical Proof Assistance** | Helping construct or verify mathematical proofs, suggesting proof strategies, identifying logical steps, and explaining proof techniques | • Mathematical research
• Education
• Theorem verification
• Proof learning
• Logic validation | • Multiple proof strategies
• Step suggestion
• Logic verification
• Explanation generation
• Educational value | • Complex proofs difficult
• Rigor limitations
• May miss subtleties
• Not formal verification
• Requires expert review | Prove √2 is irrational (by contradiction): "Assume √2 = p/q (lowest terms). Then 2 = p²/q², so 2q² = p². Thus p² is even, meaning p is even. Let p = 2k, then 2q² = 4k², so q² = 2k². Thus q² is even, meaning q is even. But if both p and q are even, they're not in lowest terms. Contradiction. Therefore √2 is irrational." | 61 | ## Multimodal Capabilities (Vision & Beyond) | **Capability** | **Description** | **Main Usages** | **Advantages** | **Considerations** | **Example** | **Reference** | |----------------|-----------------|-----------------|----------------|-------------------|-------------|---------------| | **Image Description & Captioning** | Generating natural language descriptions of image content including objects, scenes, activities, spatial relationships, and contextual details | • Accessibility (alt text)
• Content indexing
• Image search
• Social media
• Asset management | • Detailed descriptions
• Context understanding
• Multiple detail levels
• Accessibility improvement
• Automated metadata | • Accuracy varies by image type
• Subtle details may be missed
• Cultural context challenges
• Bias in descriptions
• Hallucination risks | Image of beach sunset: "A vibrant sunset over a calm ocean beach with orange and pink hues reflecting on the water. Silhouettes of two people walking along the shore, palm trees visible on the left, scattered clouds in the sky creating dramatic lighting." | 64, 65, 66 | | **Visual Question Answering (VQA)** | Answering questions about visual content by integrating image understanding with language comprehension to provide accurate, relevant responses | • Image-based Q&A
• Educational applications
• Product information
• Medical image analysis
• Visual search | • Combines vision and language
• Specific information extraction
• Natural interaction
• Complex reasoning
• Contextual answers | • Requires both modalities working well
• Reasoning complexity limits
• Fine-grained detail challenges
• Ambiguity handling
• May hallucinate details | Image of living room + Question: "What color is the couch?" Answer: "The couch is gray/charcoal colored with light blue accent pillows." OR "How many windows are visible?" Answer: "There are two large windows visible on the left wall." | 64, 65 | | **Optical Character Recognition (OCR)** | Extracting and recognizing text from images, documents, signs, screenshots, and handwritten content with high accuracy across multiple languages | • Document digitization
• Receipt scanning
• Sign reading
• Form processing
• Accessibility | • Multi-language support
• Handwriting recognition
• Layout preservation
• High accuracy
• Real-time processing | • Quality dependent on image
• Complex layouts challenging
• Handwriting accuracy varies
• Font variations
• Orientation issues | Image of business card → Extracted text: "John Smith | Senior VP of Marketing | TechCorp Inc. | john.smith@techcorp.com | +1 (555) 123-4567 | www.techcorp.com" | 66, 67, 68 | | **Document Understanding** | Comprehending structured and semi-structured documents including tables, forms, invoices, reports by analyzing layout, extracting information, and understanding document semantics | • Invoice processing
• Form extraction
• Report analysis
• Contract review
• Information extraction | • Layout awareness
• Multi-page documents
• Table extraction
• Hierarchical structure understanding
• Key-value pair extraction | • Complex layouts challenging
• Formatting variations
• Table complexity limits
• Requires validation
• Multi-column difficulties | PDF invoice → Extracted: {vendor: "ACME Corp", invoice_number: "INV-2024-001", date: "2024-01-15", total: "$1,247.50", line_items: [{desc: "Widget A", qty: 10, price: "$50.00"}, ...]} | 66, 67, 68 | | **Diagram Interpretation** | Analyzing and explaining diagrams, flowcharts, graphs, technical drawings, and visual models to extract meaning, relationships, and information | • Technical documentation
• Education
• Workflow understanding
• Data visualization
• Process analysis | • Complex relationships
• Multiple diagram types
• Structural understanding
• Text and visual integration
• Explanation generation | • Complex diagrams difficult
• Symbol interpretation
• Context dependency
• Technical accuracy
• Specialized notation challenges | Flowchart image → Description: "This workflow shows a customer order process: 1) Order received → 2) Inventory check (if available: proceed, if not: backorder) → 3) Payment processing → 4) Shipping → 5) Delivery confirmation. Decision point at inventory with two paths." | 64, 66 | | **Chart & Graph Reading** | Interpreting data visualizations including bar charts, line graphs, pie charts, scatter plots, extracting data points, trends, and insights | • Report generation
• Data analysis
• Presentation understanding
• Trend identification
• Business intelligence | • Multiple chart types
• Data extraction
• Trend identification
• Comparison analysis
• Insight generation | • Precise values challenging
• Complex visualizations difficult
• Color interpretation issues
• Scale reading accuracy
• 3D charts problematic | Bar chart image showing quarterly sales → "Q1 sales were approximately $450K, Q2 reached $580K (28.9% increase), Q3 declined to $520K (10.3% drop), Q4 rebounded to $625K (20.2% growth). Overall upward trend with strong Q4 performance." | 64, 66 | | **Image-to-Code Generation** | Converting visual designs, UI mockups, wireframes, or diagrams into executable code (HTML, CSS, frontend frameworks) | • UI development
• Prototyping
• Design-to-code
• Landing page creation
• Component generation | • Rapid prototyping
• Design fidelity
• Multiple frameworks
• Responsive code
• Reduces manual coding | • Complex layouts challenging
• Design interpretation variations
• Requires refinement
• Framework assumptions
• Accessibility considerations | UI mockup image → Generated HTML/CSS: `

Welcome

Your journey starts here

` with corresponding styles matching the design. | 66 | | **Medical Image Analysis** | Analyzing medical images (X-rays, MRIs, CT scans) to identify abnormalities, assist diagnosis, and provide clinical insights (requires specialized fine-tuning) | • Diagnostic assistance
• Radiology support
• Anomaly detection
• Triage
• Research | • Fast analysis
• Pattern detection
• Consistent evaluation
• Second opinion
• 24/7 availability | • Regulatory requirements
• High accuracy critical
• Liability concerns
• Requires medical validation
• Ethical considerations | Chest X-ray analysis: "Possible findings: 1) Mild cardiomegaly (enlarged heart), 2) No acute infiltrates, 3) No pneumothorax, 4) Costophrenic angles clear. Recommendation: Clinical correlation advised. [Note: Must be validated by radiologist]" | 64, 66 | ## Advanced & Specialized Capabilities | **Capability** | **Description** | **Main Usages** | **Advantages** | **Considerations** | **Example** | **Reference** | |----------------|-----------------|-----------------|----------------|-------------------|-------------|---------------| | **Tool Use & Function Calling** | Selecting and invoking external tools, APIs, or functions based on user requests, managing parameters, and integrating results into responses | • API integration
• Database queries
• Calculation tools
• Web searches
• System automation | • Extends capabilities dramatically
• Real-time data access
• Accurate computations
• Multi-tool orchestration
• Action execution | • API design requirements
• Error handling complexity
• Security considerations
• Tool selection accuracy
• Parameter extraction | User: "What's the weather in Paris and convert 20°C to Fahrenheit?" LLM calls: weather_api("Paris") → 20°C, then calls convert_temp(20, "C", "F") → 68°F. Response: "It's currently 20°C (68°F) in Paris." | 69, 70, 71 | | **Agentic Workflows & Planning** | Decomposing complex goals into subtasks, planning multi-step sequences, coordinating actions, and autonomously executing workflows to achieve objectives | • Task automation
• Multi-step research
• Workflow orchestration
• Goal-oriented systems
• Autonomous agents | • Complex goal achievement
• Multi-step reasoning
• Adaptive planning
• Error recovery
• Human-in-the-loop optional | • Complexity limitations
• Error propagation
• Resource management
• Hallucination risks
• Monitoring requirements | Goal: "Research competitors and create comparison report." Plan: 1) Identify top 3 competitors → 2) Search web for each → 3) Extract features/pricing → 4) Compare with our product → 5) Generate report with visualizations → 6) Save to file. | 69, 71, 72, 73 | | **Fine-Tuning & Adaptation** | Customizing pre-trained models for specific domains, tasks, or organizational needs through additional training on specialized datasets | • Domain specialization
• Enterprise customization
• Task-specific optimization
• Style adaptation
• Performance improvement | • Improved domain accuracy
• Custom behavior
• Reduced prompt engineering
• Better efficiency
• Proprietary knowledge integration | • Requires training data
• Computational resources
• Expertise needed
• Ongoing maintenance
• Overfitting risks | Medical LLM fine-tuned on clinical notes achieves 95% accuracy on diagnosis suggestion vs. 78% for base model. Understands medical terminology, protocols, and clinical reasoning patterns specific to healthcare domain. | 74, 75, 76, 77 | | **RLHF & Preference Alignment** | Training models to align with human preferences, values, and intentions using reinforcement learning from human feedback to improve helpfulness and safety | • Safety alignment
• Preference learning
• Behavior refinement
• Bias mitigation
• Quality improvement | • Human-aligned outputs
• Improved safety
• Preference learning
• Iterative improvement
• Reduced harmful outputs | • Expensive process
• Annotator bias
• Preference diversity
• Gaming rewards
• Value alignment challenges | Base model generates verbose, sometimes unhelpful responses. After RLHF: More concise, directly addresses questions, refuses harmful requests, admits uncertainty appropriately, follows instructions better. Rated 4.2/5 by users vs. 2.8/5 before. | 74, 75, 76, 77 | | **Context Window Extension** | Processing and maintaining coherence over very long sequences of text (128K, 200K, 1M+ tokens) enabling analysis of books, large codebases, or extended conversations | • Long document analysis
• Codebase understanding
• Extended conversations
• Book analysis
• Legal documents | • Handles entire documents
• No chunking needed
• Better coherence
• Full context awareness
• Reduced information loss | • Computational cost
• Memory requirements
• Attention dilution
• Slower processing
• "Lost in the middle" problem | Analyze entire 300-page contract (200K tokens) without chunking: "After reviewing the complete document, I identified 15 non-standard clauses, 3 potential conflicts between Section 4.2 and 9.7, and 7 references to outdated regulations across pages 45-182." | 78, 79, 80 | | **Speech-to-Text (ASR/Transcription)** | Converting spoken audio into written text with speaker diarization, punctuation, and timestamp capabilities across multiple languages and accents | • Meeting transcription
• Voice commands
• Accessibility
• Content indexing
• Call center analysis | • Multi-language support
• Real-time transcription
• Speaker identification
• High accuracy
• Noise robustness | • Background noise affects quality
• Accent challenges
• Domain-specific terminology
• Homophone errors
• Speaker overlap issues | Audio of business meeting → Transcript: "[00:02:15] John: The quarterly results exceeded expectations. [00:02:22] Sarah: That's great! What were the key drivers? [00:02:28] John: Primary growth came from our enterprise segment, up 34%..." | 81, 82, 83 | | **Text-to-Speech (TTS/Voice Synthesis)** | Generating natural-sounding speech audio from text input with control over voice characteristics, emotion, pace, and multi-speaker support | • Voice assistants
• Audiobook creation
• Accessibility
• IVR systems
• Content narration | • Natural prosody
• Multiple voices
• Emotion control
• Multi-language
• Real-time generation | • Uncanny valley effects
• Emotion nuance challenging
• Voice consistency
• Licensing considerations
• Computational requirements | Text: "Welcome to our store! We're excited to help you find exactly what you need today." → Generated audio with friendly, welcoming tone, appropriate pacing, natural intonation, and clear pronunciation. | 84, 85, 86 | | **Recommendation Systems** | Generating personalized recommendations by analyzing user preferences, behavior patterns, item characteristics, and collaborative filtering signals | • E-commerce personalization
• Content recommendations
• Music/movie suggestions
• Product discovery
• News curation | • Contextual recommendations
• Explanation generation
• Cold-start handling
• Multi-criteria consideration
• Conversational refinement | • Privacy concerns
• Filter bubble risks
• Diversity vs. relevance trade-off
• Bias in recommendations
• Requires user data | User profile: "Enjoys sci-fi novels, recently read 'Project Hail Mary', prefers hard sci-fi over space opera." Recommendations: "Based on your preferences, I suggest: 1) 'The Martian' (similar author, hard sci-fi), 2) 'Blindsight' (scientifically rigorous), 3) 'Rendezvous with Rama' (classic hard sci-fi exploration)." | 87, 88, 89 | | **Bias Detection & Fairness Analysis** | Identifying potential biases in text, data, or model outputs across dimensions like gender, race, age, or other protected attributes | • Content moderation
• Fairness auditing
• Model evaluation
• Inclusive language
• Compliance checking | • Multiple bias types
• Automated detection
• Quantifiable metrics
• Mitigation suggestions
• Awareness raising | • Subjective definitions
• Context dependency
• Cultural variations
• May miss subtle biases
• False positives | Text: "The doctor made his diagnosis after consulting with the nurse and her patient records." Analysis: "Potential gender stereotyping detected: Associates 'doctor' with male pronoun (his) and 'nurse' with female pronoun (her). Consider gender-neutral language or avoiding pronouns." | 26, 27 | | **Prompt Engineering Assistance** | Helping users craft effective prompts through suggestions, templates, optimization, and iterative refinement to improve LLM output quality | • Prompt optimization
• Template creation
• Output improvement
• Learning assistance
• Best practices | • Improves results significantly
• Reduces trial-and-error
• Educational value
• Pattern recognition
• Saves time | • Meta-level complexity
• Task-dependent strategies
• Model-specific techniques
• Experimentation still needed
• Diminishing returns | Original prompt: "Summarize this." Improved: "Provide a 150-word executive summary of the following document, focusing on key findings, main conclusions, and actionable recommendations. Format as bullet points for easy scanning. Document: [text]" → Better, more consistent results. | 90, 91 | | **Low-Resource Language Support** | Providing capabilities for languages with limited training data through cross-lingual transfer, multilingual models, and translation intermediaries | • Endangered languages
• Regional dialects
• Translation
• Accessibility
• Cultural preservation | • Broader language coverage
• Cross-lingual transfer
• Cultural inclusion
• No extensive data needed
• Globalization support | • Lower accuracy than high-resource languages
• Limited training data
• Cultural context challenges
• Evaluation difficulty
• Resource constraints | Translate English to Quechua (low-resource): "The meeting will be held tomorrow" → "Kay huñunakuyqa qhayankama rurasqa kanqa" [Note: Quality lower than English↔Spanish, but enables basic communication] | 7, 8, 9 | ## References ### Official Documentation & Foundational Resources 1. Wikipedia - Large Language Model. Retrieved from: https://en.wikipedia.org/wiki/Large_language_model 2. Elastic - Understanding Large Language Models: A Comprehensive Guide. Retrieved from: https://www.elastic.co/what-is/large-language-models 3. IBM - What Are Large Language Models (LLMs)? Retrieved from: https://www.ibm.com/think/topics/large-language-models 4. arXiv - A Comprehensive Survey on Automatic Text Summarization with Exploration of LLM-Based Methods (2403.02901v2). Retrieved from: https://arxiv.org/html/2403.02901v2 5. ProjectPro - LLM Summarization: Techniques, Metrics, and Top Models. Retrieved from: https://www.projectpro.io/article/llm-summarization/1082 6. AWS Machine Learning Blog - Techniques for Automatic Summarization of Documents Using Language Models. Retrieved from: https://aws.amazon.com/blogs/machine-learning/techniques-for-automatic-summarization-of-documents-using-language-models/ ### Translation & Multilingual Capabilities 7. ScienceDirect - A Survey of Multilingual Large Language Models. Retrieved from: https://www.sciencedirect.com/science/article/pii/S2666389924002903 8. MDPI - Machine Translation in the Era of Large Language Models. Retrieved from: https://www.mdpi.com/2078-2489/16/9/723 9. Microsoft Learn - Use AI and Large Language Models for Translation. Retrieved from: https://learn.microsoft.com/en-us/globalization/localization/ai/ai-and-llms-for-translation ### Style Transfer & Text Rewriting 10. arXiv - RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting (2305.15685v2). Retrieved from: https://arxiv.org/html/2305.15685v2 11. AAAI - RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting. Retrieved from: https://ojs.aaai.org/index.php/AAAI/article/view/29863/31505 ### Grammar & Language Correction 12. QuillBot - Free AI Grammar Checker. Retrieved from: https://quillbot.com/grammar-check 13. WProofreader Blog - Generative AI and LLMs: A New Trend of Spelling and Grammar Checkers. Retrieved from: https://blog.wproofreader.com/generative-ai-and-llms-a-new-trend-of-spelling-and-grammar-checkers-part-1/ ### Question Answering & Knowledge Retrieval 14. GeeksforGeeks - What is a Large Language Model (LLM). Retrieved from: https://www.geeksforgeeks.org/artificial-intelligence/large-language-model-llm/ 15. AWS - What is LLM? Large Language Models Explained. Retrieved from: https://aws.amazon.com/what-is/large-language-model/ 16. arXiv - Advancing Reasoning in Large Language Models (2502.03671v1). Retrieved from: https://arxiv.org/html/2502.03671v1 ### Reasoning & Logic 17. DigitalOcean - Understanding Reasoning in Large Language Models. Retrieved from: https://www.digitalocean.com/community/tutorials/understanding-reasoning-in-llms 18. arXiv - Empowering LLMs with Logical Reasoning: A Comprehensive Survey (2502.15652v2). Retrieved from: https://arxiv.org/html/2502.15652v2 ### Chain-of-Thought Reasoning 19. DataCamp - Chain-of-Thought Prompting: Step-by-Step Reasoning with LLMs. Retrieved from: https://www.datacamp.com/tutorial/chain-of-thought-prompting 20. Prompt Engineering Guide - Chain-of-Thought Prompting. Retrieved from: https://www.promptingguide.ai/techniques/cot 21. arXiv - Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2201.11903). Retrieved from: https://arxiv.org/abs/2201.11903 ### Retrieval-Augmented Generation (RAG) 22. Wikipedia - Retrieval-Augmented Generation. Retrieved from: https://en.wikipedia.org/wiki/Retrieval-augmented_generation 23. Google Cloud - What is Retrieval-Augmented Generation (RAG)? Retrieved from: https://cloud.google.com/use-cases/retrieval-augmented-generation 24. Microsoft Learn - RAG and Generative AI - Azure AI Search. Retrieved from: https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview 25. AWS - What is RAG? Retrieval-Augmented Generation AI Explained. Retrieved from: https://aws.amazon.com/what-is/retrieval-augmented-generation/ ### Hallucination & Fact-Checking 26. arXiv - LLM-Check: Investigating Detection of Hallucinations in Large Language Models. Retrieved from: https://openreview.net/pdf?id=LYx4w3CAgy 27. arXiv - Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation (2508.03860v1). Retrieved from: https://arxiv.org/html/2508.03860v1 28. vLLM Blog - Token-Level Truth: Real-Time Hallucination Detection for Production LLMs. Retrieved from: https://blog.vllm.ai/2025/12/14/halugate.html ### Embeddings & Semantic Similarity 29. Neptune.ai - What are LLM Embeddings: All you Need to Know. Retrieved from: https://neptune.ai/blog/what-are-llm-embeddings 30. Keymakr - Vector Embeddings: Semantic Search & LLM Integration. Retrieved from: https://keymakr.com/blog/vector-embeddings-explained-semantic-search-llm-integration-guide/ 31. arXiv - LLMs are Also Effective Embedding Models (2412.12591v1). Retrieved from: https://arxiv.org/html/2412.12591v1 ### Code Generation & Programming 32. GitHub - GitHub Copilot: Your AI Pair Programmer. Retrieved from: https://github.com/features/copilot 33. arXiv - A Survey on Code Generation with LLM-based Agents (2508.00083v1). Retrieved from: https://arxiv.org/html/2508.00083v1 34. Medium - Automated Code Generation with Large Language Models (LLMs). Retrieved from: https://medium.com/@sunnypatel124555/automated-code-generation-with-large-language-models-llms-0ad32f4b37c8 35. arXiv - Enhancing the Code Debugging Ability of LLMs via Communicative Agent Based Data Refinement (2408.05006v1). Retrieved from: https://arxiv.org/html/2408.05006v1 36. ICLR 2024 - Teaching Large Language Models to Self-Debug. Retrieved from: https://proceedings.iclr.cc/paper_files/paper/2024/file/2460396f2d0d421885997dd1612ac56b-Paper-Conference.pdf 37. ZenCoder - 6 Best LLMs for Coding To Try in 2026. Retrieved from: https://zencoder.ai/blog/best-llm-for-coding ### Documentation & Technical Writing 38. arXiv - Technical Documentation and API Documentation Generation (related research). Multiple sources consolidated. ### Text-to-SQL 39. AWS Machine Learning Blog - Enterprise-Grade Natural Language to SQL Generation Using LLMs. Retrieved from: https://aws.amazon.com/blogs/machine-learning/enterprise-grade-natural-language-to-sql-generation-using-llms-balancing-accuracy-latency-and-scale/ 40. Promethium - LLM & AI Models for Text-to-SQL. Retrieved from: https://promethium.ai/guides/llm-ai-models-text-to-sql/ ### Sentiment Analysis & Text Classification 41. Sapien - Text Data Labeling: Techniques for Named Entity Recognition and Sentiment Analysis. Retrieved from: https://www.sapien.io/blog/text-data-labeling-techniques-for-named-entity-recognition-and-sentiment-analysis 42. Kili Technology - Understanding Named Entity Recognition & Text Classification. Retrieved from: https://kili-technology.com/blog/understanding-named-entity-recognition-text-classification ### Named Entity Recognition 43. Encord - Named Entity Recognition (NER): Ultimate Guide. Retrieved from: https://encord.com/blog/named-entity-recognition/ 44. IBM - What Is Named Entity Recognition? Retrieved from: https://www.ibm.com/think/topics/named-entity-recognition 45. GitHub - Spacy-LLM: Integrating LLMs into Structured NLP Pipelines. Retrieved from: https://github.com/explosion/spacy-llm 46. arXiv - GPT-NER: Named Entity Recognition via Large Language Models (2304.10428). Retrieved from: https://arxiv.org/abs/2304.10428 ### Creative Writing & Content Generation 47. SiliconFlow - Ultimate Guide: The Best Open Source LLM For Creative Writing & Ideation. Retrieved from: https://www.siliconflow.com/articles/en/best-open-source-llm-for-creative-writing-ideation 48. Intellectual Lead - Best LLMs for Writing in 2026. Retrieved from: https://intellectualead.com/best-llm-writing/ 49. ACM Digital Library - Wordcraft: Story Writing With Large Language Models. Retrieved from: https://dl.acm.org/doi/fullHtml/10.1145/3490099.3511105 50. LessWrong - Creative Writing with LLMs, Part 1: Prompting for Fiction. Retrieved from: https://www.lesswrong.com/posts/D9MHrR8GrgSbXMqtB/creative-writing-with-llms-part-1-prompting-for-fiction ### Structured Output Generation 51. Medium - Structured Output Generation in LLMs: JSON Schema and Grammar-Based Decoding. Retrieved from: https://medium.com/@emrekaratas-ai/structured-output-generation-in-llms-json-schema-and-grammar-based-decoding-6a5c58b698a6 52. GitHub - Awesome LLM JSON: Resource List for Generating JSON Using LLMs. Retrieved from: https://github.com/imaurer/awesome-llm-json 53. Agenta - The Guide to Structured Outputs and Function Calling with LLMs. Retrieved from: https://agenta.ai/blog/the-guide-to-structured-outputs-and-function-calling-with-llms 54. arXiv - Let Me Speak Freely? A Study on the Impact of Format Restrictions (2408.02442v1). Retrieved from: https://arxiv.org/html/2408.02442v1 ### Conversational AI & Dialogue 55. arXiv - Evaluating LLM-based Agents for Multi-Turn Conversations (2503.22458). Retrieved from: https://arxiv.org/pdf/2503.22458 56. ACL Anthology - Evaluating Very Long-Term Conversational Memory of LLM Agents. Retrieved from: https://aclanthology.org/2024.acl-long.747.pdf 57. arXiv - A Survey on Multi-Turn Interaction Capabilities of Large Language Models (2501.09959v1). Retrieved from: https://arxiv.org/html/2501.09959v1 58. DataCamp - How Does LLM Memory Work? Building Context-Aware AI Applications. Retrieved from: https://www.datacamp.com/blog/how-does-llm-memory-work 59. arXiv - Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning. Retrieved from: https://arxiv.org/pdf/2511.00222 ### Mathematical Capabilities 60. arXiv - Evaluation of LLMs for Mathematical Problem Solving (2506.00309v1). Retrieved from: https://arxiv.org/html/2506.00309v1 61. arXiv - Large Language Models for Mathematical Reasoning: Progresses and Challenges (2402.00157v1). Retrieved from: https://arxiv.org/html/2402.00157v1 62. Designs Valley - Best LLMs for Math & Problem Solving. Retrieved from: https://designsvalley.com/best-llm-for-math/ 63. Medium - Doing Math with Large Language Models. Retrieved from: https://medium.com/google-cloud/doing-math-with-large-language-models-69d94c8b0590 ### Multimodal & Vision Capabilities 64. Mistral AI - Mistral OCR. Retrieved from: https://mistral.ai/news/mistral-ocr 65. Medium - Beyond OCR: How Multimodal AI is Changing the Game of Image Understanding. Retrieved from: https://medium.com/capgemini-invent-lab/from-ocr-to-multimodal-a-new-era-in-image-to-text-technology-8d45d7559f01 66. arXiv - OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models (2305.07895v5). Retrieved from: https://arxiv.org/html/2305.07895v5 67. Grab Engineering - How We Built a Custom Vision LLM to Improve Document Processing. Retrieved from: https://engineering.grab.com/custom-vision-llm-at-grab 68. Modal - 8 Top Open-Source OCR Models Compared. Retrieved from: https://modal.com/blog/8-top-open-source-ocr-models-compared ### Tool Use & Function Calling 69. LangChain Docs - Workflows and Agents. Retrieved from: https://docs.langchain.com/oss/python/langgraph/workflows-agents 70. Temporal Documentation - Tool Calling Agent. Retrieved from: https://docs.temporal.io/ai-cookbook/tool-call-openai-python 71. Weaviate - What Are Agentic Workflows? Patterns, Memory, Use Cases, and Examples. Retrieved from: https://weaviate.io/blog/what-are-agentic-workflows ### Planning & Task Orchestration 72. arXiv - Routine: A Structural Planning Framework for LLM Agent System in Enterprise (2507.14447). Retrieved from: https://arxiv.org/pdf/2507.14447 73. AWS Prescriptive Guidance - Workflow for Orchestration. Retrieved from: https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/workflow-for-orchestration.html ### Fine-Tuning & Model Adaptation 74. HuggingFace - Fine-tuning 20B LLMs with RLHF on a 24GB Consumer GPU. Retrieved from: https://huggingface.co/blog/trl-peft 75. arXiv - The Ultimate Guide to Fine-Tuning LLMs (2408.13296v1). Retrieved from: https://arxiv.org/html/2408.13296v1 76. Synthesis AI - Fine-Tuning LLMs: RLHF, LoRA, and Instruction Tuning. Retrieved from: https://synthesis.ai/2024/08/13/fine-tuning-llms-rlhf-lora-and-instruction-tuning/ 77. DigitalOcean - LLM Fine-Tuning: A Guide for Domain-Specific Models. Retrieved from: https://www.digitalocean.com/community/tutorials/llm-finetuning-domain-specific-models ### Context Windows & Long-Context 78. IBM Research - Why Larger LLM Context Windows Are All the Rage. Retrieved from: https://research.ibm.com/blog/larger-context-window 79. Content Whale - How Context Engineering Improves LLM Memory and Response Accuracy. Retrieved from: https://content-whale.com/us/blog/llm-context-engineering-information-retention/ 80. Coding Scape - LLMs with Largest Context Windows. Retrieved from: https://codingscape.com/blog/llms-with-largest-context-windows ### Speech-to-Text (ASR) 81. VideoSDK - Voice to Text LLM Model: Revolutionizing Speech Recognition. Retrieved from: https://www.videosdk.live/developer-hub/llm/voice-to-text-llm-model 82. LiveKit Documentation - Speech-to-Text (STT) Models Overview. Retrieved from: https://docs.livekit.io/agents/models/stt/ 83. arXiv - Prompting Large Language Models with Speech Recognition Abilities (2307.11795). Retrieved from: https://arxiv.org/abs/2307.11795 ### Text-to-Speech (TTS) 84. Bland AI - Reimagining TTS with LLM-Powered Audio Generation. Retrieved from: https://www.bland.ai/blogs/new-tts-announcement 85. BentoML - The Best Open-Source Text-to-Speech Models in 2026. Retrieved from: https://www.bentoml.com/blog/exploring-the-world-of-open-source-text-to-speech-models 86. HuggingFace Blog - LLM Based Audio Models. Retrieved from: https://huggingface.co/blog/YatharthS/llm-tts-models ### Recommendation Systems 87. arXiv - LLM-Enhanced Collaborative Filtering for Personalized Recommendation (391411111). Retrieved from: https://www.researchgate.net/publication/391411111_LLM-Enhanced_Collaborative_Filtering_for_Personalized_Recommendation 88. Teradata - LLMs for Recommendation Systems. 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YouTube - Large Language Models (LLMs) - Everything You NEED To Know. Retrieved from: https://www.youtube.com/watch?v=osKyvYJ3PRM 95. YouTube - How Large Language Models Work by IBM Technology. Retrieved from: https://www.youtube.com/watch?v=5sLYAQS9sWQ

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