{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-dair-ai--prompt-engineering-guide","slug":"dair-ai--prompt-engineering-guide","name":"Prompt-Engineering-Guide","type":"prompt","url":"https://www.promptingguide.ai/","page_url":"https://unfragile.ai/dair-ai--prompt-engineering-guide","categories":["prompt-engineering","rag-knowledge"],"tags":["agent","agents","ai-agents","chatgpt","deep-learning","generative-ai","language-model","llms","openai","prompt-engineering","rag"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-dair-ai--prompt-engineering-guide__cap_0","uri":"capability://text.generation.language.multi.language.prompt.engineering.documentation.with.mdx.based.content.delivery","name":"multi-language prompt engineering documentation with mdx-based content delivery","description":"Serves comprehensive prompt engineering educational content across 11 languages using Next.js 13 with Nextra 2.13 static site generation. The platform uses MDX files as the source of truth, enabling interactive code examples, embedded notebooks, and dynamic content rendering while maintaining a single source for all language variants through i18n middleware. Content is organized hierarchically across 745+ pages covering foundational to advanced prompting techniques.","intents":["Learn prompt engineering fundamentals and advanced techniques in my native language","Access structured, searchable documentation on LLM prompting strategies without paywalls","Find code examples and interactive notebooks demonstrating specific prompting patterns","Understand how different prompting techniques compare and when to apply each one"],"best_for":["AI practitioners and developers learning prompt engineering systematically","Non-English speaking teams adopting LLM technologies","Educators building curriculum around LLM capabilities","Open-source contributors extending prompt engineering knowledge"],"limitations":["Static site generation means real-time updates require rebuild and redeploy cycle","No built-in interactive prompt testing environment — examples are read-only documentation","Language translations depend on community contributions; some languages may lag behind English","Content organization is fixed by MDX file structure; dynamic content filtering/search is limited to client-side"],"requires":["Web browser with JavaScript enabled","No authentication required","Internet connection to access hosted platform at promptingguide.ai"],"input_types":["MDX files (Markdown + JSX)","Jupyter notebooks (.ipynb)","PNG/JPG images for diagrams and examples"],"output_types":["HTML rendered documentation pages","Interactive React components embedded in pages","Downloadable Jupyter notebooks","Structured navigation and search results"],"categories":["text-generation-language","educational-content","documentation-platform"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_1","uri":"capability://planning.reasoning.chain.of.thought.cot.prompting.technique.documentation.and.examples","name":"chain-of-thought (cot) prompting technique documentation and examples","description":"Provides structured educational content explaining Chain-of-Thought prompting methodology, which breaks down complex reasoning tasks into intermediate steps. The guide documents the theoretical foundation, implementation patterns, and practical examples showing how CoT improves LLM accuracy on multi-step reasoning problems. Content includes worked examples demonstrating step-by-step reasoning decomposition.","intents":["Understand how Chain-of-Thought prompting works and why it improves reasoning accuracy","Learn the difference between zero-shot and few-shot CoT variants","See concrete examples of CoT prompts applied to math, logic, and reasoning tasks","Implement CoT in my own LLM applications to improve output quality"],"best_for":["Developers building reasoning-heavy LLM applications (math solvers, logic engines)","Data scientists improving LLM accuracy on complex tasks","Researchers studying LLM reasoning capabilities","Teams migrating from simple prompts to structured reasoning workflows"],"limitations":["Documentation is educational reference material, not executable code — requires manual implementation","CoT adds latency because it forces multi-step token generation; no guidance on latency-accuracy tradeoffs","Examples focus on English language; applicability to other languages not thoroughly documented","No built-in cost analysis — CoT increases token consumption but guide doesn't quantify this"],"requires":["Understanding of basic prompt engineering concepts","Access to an LLM API (OpenAI, Anthropic, or open-source models)","Ability to read and adapt code examples from documentation"],"input_types":["Text descriptions of reasoning tasks","Example prompts in Markdown format","Jupyter notebooks with CoT implementations"],"output_types":["Structured documentation pages explaining CoT theory","Code examples showing CoT prompt construction","Comparative examples showing CoT vs non-CoT outputs"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_10","uri":"capability://safety.moderation.adversarial.prompting.and.defense.techniques.documentation","name":"adversarial prompting and defense techniques documentation","description":"Documents adversarial prompting attacks (prompt injection, jailbreaking, manipulation) and defense strategies to make LLM systems robust. The guide explains attack vectors like instruction override, context confusion, and output manipulation, along with defensive techniques like input validation, output filtering, and prompt hardening.","intents":["Understand how adversarial prompts can manipulate LLM behavior and bypass safety measures","Learn defense strategies to protect LLM applications from prompt injection attacks","Design prompts that are resistant to adversarial manipulation","Test and validate LLM system robustness against known attack patterns"],"best_for":["Security teams building production LLM applications","Developers implementing user-facing LLM systems","Researchers studying LLM robustness and adversarial examples","Teams deploying LLMs in high-stakes domains (finance, healthcare, legal)"],"limitations":["Adversarial techniques evolve faster than defenses; documentation may lag behind new attack methods","No comprehensive evaluation framework for defense effectiveness","Many defenses are heuristic-based and can be circumvented with creative attacks","Documentation focuses on prompt-level defenses; doesn't address system-level security (access control, audit logging)"],"requires":["Understanding of LLM capabilities and failure modes","Security mindset and threat modeling experience","Ability to test and validate defenses","Monitoring and logging infrastructure"],"input_types":["Adversarial prompt examples","System prompts and instructions","User inputs to validate"],"output_types":["Attack demonstrations showing vulnerabilities","Defense recommendations and hardened prompts","Security test results"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_11","uri":"capability://text.generation.language.llm.model.comparison.and.selection.guidance.across.providers.and.architectures","name":"llm model comparison and selection guidance across providers and architectures","description":"Provides structured documentation comparing LLM capabilities across providers (OpenAI, Anthropic, open-source) and architectures (GPT-4, Claude, Llama, etc.), covering performance characteristics, cost, context window, and specialized capabilities. The guide helps developers select appropriate models for specific use cases based on task requirements and constraints.","intents":["Choose the right LLM for my specific task based on capabilities and constraints","Understand performance differences between models and when to upgrade","Compare cost-effectiveness of different models for my workload","Learn about specialized models for specific domains (code, math, multimodal)"],"best_for":["Developers evaluating LLM options for new projects","Teams optimizing LLM costs and performance","Researchers comparing model capabilities","Builders deciding between closed-source and open-source models"],"limitations":["Model capabilities and pricing change frequently; documentation may become outdated","No automated benchmarking or evaluation framework — comparisons are static","Limited guidance on how to evaluate models for your specific task","Doesn't address model availability, latency, or regional deployment constraints"],"requires":["Understanding of LLM capabilities and limitations","Access to model documentation and pricing information","Ability to run benchmarks on your specific tasks"],"input_types":["Task requirements and constraints","Performance and cost criteria","Model capability specifications"],"output_types":["Model comparison tables and matrices","Recommendations for specific use cases","Cost and performance analysis"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_12","uri":"capability://tool.use.integration.function.calling.and.tool.integration.patterns.for.llm.agents","name":"function calling and tool integration patterns for llm agents","description":"Documents function calling capabilities that enable LLMs to invoke external tools and APIs by generating structured function calls. The guide explains how to define function schemas, parse LLM function call outputs, handle execution results, and integrate function calling into agent loops for tool-augmented reasoning.","intents":["Enable LLMs to call external functions and APIs to gather information or perform actions","Design function schemas that LLMs can reliably invoke","Implement error handling for function call failures","Build agents that combine reasoning with tool use through function calling"],"best_for":["Developers building LLM agents with tool access","Teams implementing knowledge workers that need external data access","Builders creating autonomous systems that take actions in external systems","Researchers studying tool grounding and agent capabilities"],"limitations":["LLM function calling quality varies by model; some models hallucinate function calls","Requires careful schema design — poorly designed schemas lead to incorrect function calls","No guidance on handling function call failures or retries","Documentation assumes function calling is available in the LLM API; not all models support it"],"requires":["LLM API with function calling support (OpenAI, Anthropic, etc.)","Ability to define and expose functions/APIs for LLM use","Infrastructure to execute function calls and return results","Error handling and retry logic"],"input_types":["Function definitions and schemas","LLM-generated function call specifications","Function execution results"],"output_types":["Structured function call specifications","Function execution results","Agent responses incorporating function results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_13","uri":"capability://memory.knowledge.context.engineering.for.ai.agents.with.memory.and.state.management","name":"context engineering for ai agents with memory and state management","description":"Documents context engineering practices for building effective AI agents, including how to structure system prompts, manage conversation history, implement memory systems, and handle context window constraints. The guide covers techniques for maintaining agent state, prioritizing relevant context, and designing prompts that enable agents to reason effectively within limited context windows.","intents":["Design system prompts and context that enable agents to reason effectively","Manage conversation history and memory to maintain agent state across interactions","Optimize context usage to fit within model context window limits","Implement memory systems (short-term, long-term) for agents"],"best_for":["Developers building multi-turn conversational agents","Teams implementing long-running agents with memory requirements","Researchers studying agent architecture and context management","Builders optimizing agent performance within context constraints"],"limitations":["Context window limits are model-specific and constantly changing; guidance may become outdated","No automated context optimization — requires manual prompt engineering","Memory systems require external storage; documentation doesn't cover persistence options","Limited guidance on context prioritization strategies for very long conversations"],"requires":["Understanding of LLM context windows and token limits","Ability to design and structure system prompts","Infrastructure for storing and retrieving conversation history","Mechanism to summarize or compress context when needed"],"input_types":["System prompt specifications","Conversation history and turns","Memory/state data","Context window constraints"],"output_types":["Optimized system prompts","Context-aware agent responses","Memory state updates"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_14","uri":"capability://data.processing.analysis.synthetic.dataset.generation.using.llms.for.training.and.evaluation","name":"synthetic dataset generation using llms for training and evaluation","description":"Documents techniques for using LLMs to generate synthetic training data, evaluation datasets, and test cases. The guide covers prompt engineering for data generation, quality control strategies, and how to use synthetic data for fine-tuning, evaluation, and testing LLM applications.","intents":["Generate training data for fine-tuning models without manual labeling","Create diverse evaluation datasets to test LLM application robustness","Generate test cases and edge cases for comprehensive testing","Augment limited labeled data with synthetic examples"],"best_for":["Teams with limited labeled data for fine-tuning","Developers building evaluation datasets for LLM applications","Researchers studying synthetic data quality and effectiveness","Builders needing diverse test cases for robustness testing"],"limitations":["Synthetic data quality depends on generation prompts; poor prompts produce low-quality data","Risk of distribution shift — synthetic data may not match real-world data distribution","No automated quality assessment; requires manual review or evaluation metrics","Synthetic data may amplify biases from the generating LLM"],"requires":["LLM API for data generation","Clear specifications for desired data characteristics","Quality control process (manual review or automated metrics)","Evaluation framework to assess synthetic data utility"],"input_types":["Data generation prompts and specifications","Seed examples or templates","Quality criteria and constraints"],"output_types":["Generated synthetic examples","Quality assessment results","Datasets ready for training or evaluation"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_15","uri":"capability://text.generation.language.fine.tuning.guidance.for.gpt.4o.and.other.models.with.prompt.engineering.integration","name":"fine-tuning guidance for gpt-4o and other models with prompt engineering integration","description":"Documents fine-tuning approaches for adapting LLMs to specific tasks, including when to fine-tune vs use prompt engineering, how to prepare training data, and how to combine fine-tuning with advanced prompting techniques. The guide covers fine-tuning for GPT-4o and discusses tradeoffs between fine-tuning and in-context learning.","intents":["Decide whether to fine-tune a model or use prompt engineering for your task","Prepare training data and implement fine-tuning pipelines","Combine fine-tuning with prompt engineering for optimal results","Evaluate fine-tuned models and iterate on training data"],"best_for":["Teams with domain-specific tasks requiring model adaptation","Developers optimizing model performance for production applications","Researchers studying fine-tuning effectiveness and data efficiency","Builders with sufficient labeled data to justify fine-tuning costs"],"limitations":["Fine-tuning requires significant labeled data; guidance on minimum data requirements is limited","Fine-tuning cost and latency tradeoffs not thoroughly analyzed","Documentation focuses on OpenAI models; applicability to other providers varies","Limited guidance on detecting overfitting or when fine-tuning is not helping"],"requires":["Labeled training data (typically hundreds to thousands of examples)","Fine-tuning API access (OpenAI, etc.)","Evaluation dataset for assessing fine-tuned model performance","Computational budget for fine-tuning and inference"],"input_types":["Training examples with inputs and expected outputs","Fine-tuning hyperparameters","Evaluation dataset"],"output_types":["Fine-tuned model checkpoint","Performance metrics on evaluation set","Comparison with base model"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_16","uri":"capability://text.generation.language.interactive.jupyter.notebook.examples.for.hands.on.prompt.engineering.practice","name":"interactive jupyter notebook examples for hands-on prompt engineering practice","description":"Provides executable Jupyter notebooks demonstrating prompt engineering techniques with runnable code examples. Notebooks cover techniques like CoT, PAL, adversarial prompting, and RAG with actual LLM API calls, enabling learners to experiment and modify examples in real-time.","intents":["Learn prompt engineering by running and modifying working code examples","Experiment with different prompts and techniques interactively","Understand how techniques work in practice with real LLM outputs","Build on provided examples to solve my own problems"],"best_for":["Developers learning prompt engineering through hands-on practice","Teams prototyping LLM applications quickly","Researchers experimenting with prompting techniques","Educators teaching prompt engineering with interactive examples"],"limitations":["Notebooks require API keys and incur costs for LLM API calls","Examples may use specific models (GPT-3.5, GPT-4) that may change or become unavailable","Notebook outputs depend on model behavior which can vary; results may not be reproducible","Limited to Jupyter environment; not suitable for production deployment"],"requires":["Jupyter notebook environment (local or cloud-based)","Python 3.7+","LLM API key (OpenAI, Anthropic, etc.)","Required Python packages (openai, requests, etc.)"],"input_types":["Notebook cells with prompt engineering code","Task descriptions and examples","Model parameters and configurations"],"output_types":["LLM-generated outputs from running prompts","Analysis and visualization of results","Modified notebooks with custom experiments"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_17","uri":"capability://memory.knowledge.research.papers.and.findings.collection.on.prompt.engineering.rag.and.agents","name":"research papers and findings collection on prompt engineering, rag, and agents","description":"Curates and summarizes research papers on prompt engineering, RAG, LLM agents, and related topics, providing links to original papers and distilled summaries of key findings. The collection helps practitioners stay current with research advances and understand the theoretical foundations of prompting techniques.","intents":["Stay current with latest research on prompt engineering and LLM techniques","Understand the theoretical foundations of prompting methods","Find citations and references for my own research or applications","Learn about emerging techniques and approaches from academic literature"],"best_for":["Researchers studying prompt engineering and LLM capabilities","Developers wanting to understand the science behind techniques","Teams evaluating new techniques before implementation","Academics building on existing research"],"limitations":["Paper summaries are curated by community; may be incomplete or biased","Research moves faster than documentation updates; new papers may not be included immediately","Summaries are simplified; readers should review original papers for full details","Limited to papers that have been published and discovered by maintainers"],"requires":["Understanding of machine learning and LLM concepts","Access to paper links (arXiv, ACL, etc.)","Ability to read and understand academic papers"],"input_types":["Research paper links and metadata","Paper summaries and key findings","Related technique references"],"output_types":["Curated paper collection organized by topic","Paper summaries and key takeaways","Links to original papers"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_2","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.technique.documentation.with.architecture.patterns","name":"retrieval augmented generation (rag) technique documentation with architecture patterns","description":"Documents RAG methodology for augmenting LLM responses with retrieved external knowledge, explaining the three-stage pipeline: retrieval (finding relevant documents), augmentation (injecting context into prompts), and generation (LLM producing grounded responses). The guide covers architectural patterns for building RAG systems, including vector store integration, retrieval ranking strategies, and context window management.","intents":["Understand RAG architecture and when to use it instead of fine-tuning or in-context learning","Learn how to structure retrieval pipelines to feed relevant context to LLMs","See examples of RAG applied to knowledge-heavy domains (customer support, documentation QA)","Design RAG systems that balance retrieval accuracy, latency, and cost"],"best_for":["Teams building knowledge-grounded chatbots and QA systems","Developers implementing document-based search with LLM reasoning","Organizations migrating from traditional search to semantic retrieval","Researchers studying grounding and factuality in LLM outputs"],"limitations":["Documentation covers theory and patterns but not specific vector database implementations (Pinecone, Weaviate, etc.)","No guidance on retrieval evaluation metrics or how to measure RAG quality","Assumes familiarity with embeddings and semantic search concepts","Limited discussion of RAG failure modes (retrieval errors, context contradiction, hallucination despite grounding)"],"requires":["Understanding of embeddings and vector similarity","Knowledge of document chunking and indexing strategies","Access to vector storage infrastructure (cloud or self-hosted)","LLM API access for generation stage"],"input_types":["Unstructured documents (PDFs, web pages, text files)","Structured queries or questions","Document metadata for filtering"],"output_types":["Retrieved document chunks ranked by relevance","Augmented prompts with injected context","LLM-generated responses grounded in retrieved documents"],"categories":["memory-knowledge","search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_3","uri":"capability://planning.reasoning.react.reasoning.acting.framework.documentation.with.agent.loop.patterns","name":"react (reasoning + acting) framework documentation with agent loop patterns","description":"Explains the ReAct framework that combines reasoning (chain-of-thought) with acting (tool use), enabling LLMs to iteratively think, act, and observe in a loop. The guide documents the ReAct prompt structure, how to integrate external tools/APIs, and how to manage the reasoning-action-observation cycle. Content shows how ReAct enables agents to solve complex tasks requiring multiple tool invocations.","intents":["Build LLM agents that can reason about problems and call tools to gather information","Understand the ReAct loop structure and how to implement it in code","Learn how to design tool interfaces that work well with ReAct agents","Debug agent behavior when reasoning or tool calls fail"],"best_for":["Developers building autonomous LLM agents for task automation","Teams implementing multi-step workflows that require reasoning and tool use","Researchers studying agent architectures and tool grounding","Builders creating agents for knowledge work (research, analysis, planning)"],"limitations":["Documentation explains ReAct pattern but doesn't provide production-ready agent framework code","No guidance on managing agent state, memory, or long-running conversations","Limited discussion of failure recovery — what happens when tool calls fail or return unexpected results","Assumes tools are well-defined and reliable; doesn't address tool hallucination or incorrect tool selection"],"requires":["Understanding of LLM prompting and chain-of-thought reasoning","Ability to define and expose tools/APIs for agent use","LLM API that supports structured output or function calling","Infrastructure to execute tool calls and return results to agent"],"input_types":["Natural language task descriptions","Tool definitions and schemas","Observation data from tool execution"],"output_types":["Reasoning traces showing agent thought process","Tool call specifications with parameters","Final answers grounded in tool observations"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_4","uri":"capability://planning.reasoning.tree.of.thoughts.tot.advanced.reasoning.technique.documentation","name":"tree of thoughts (tot) advanced reasoning technique documentation","description":"Documents Tree of Thoughts methodology that explores multiple reasoning paths simultaneously rather than a single linear chain, enabling LLMs to backtrack and explore alternative solutions. The guide explains the ToT search strategy (breadth-first, depth-first, beam search), how to evaluate intermediate reasoning states, and when ToT outperforms simpler techniques like CoT.","intents":["Understand when to use Tree of Thoughts instead of Chain-of-Thought for complex problems","Learn how to structure multi-path reasoning for problems with multiple solution approaches","Implement ToT search strategies that balance exploration and computational cost","See examples of ToT applied to planning, puzzle-solving, and creative tasks"],"best_for":["Developers solving complex reasoning problems requiring exploration (planning, puzzle-solving)","Teams working on tasks where multiple solution paths exist and need evaluation","Researchers studying advanced LLM reasoning capabilities","Builders optimizing for solution quality over latency"],"limitations":["ToT significantly increases token consumption and latency compared to CoT due to exploring multiple paths","Documentation doesn't provide cost-benefit analysis or guidance on when ToT ROI justifies the overhead","Evaluating intermediate reasoning states requires domain-specific heuristics; no general evaluation framework provided","Computational complexity grows exponentially with tree depth; no guidance on practical depth limits"],"requires":["Understanding of Chain-of-Thought and basic reasoning techniques","LLM API with sufficient context window to handle multiple reasoning paths","Ability to define evaluation heuristics for intermediate states","Computational budget for exploring multiple reasoning branches"],"input_types":["Complex reasoning tasks with multiple solution paths","Evaluation criteria for intermediate states","Search strategy parameters (branching factor, depth limit)"],"output_types":["Reasoning tree with multiple explored paths","Evaluated intermediate states with scores","Final solution selected from best path"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_5","uri":"capability://planning.reasoning.self.consistency.prompting.technique.for.improving.reasoning.reliability","name":"self-consistency prompting technique for improving reasoning reliability","description":"Explains Self-Consistency methodology that samples multiple reasoning paths from an LLM and aggregates results through majority voting or weighted consensus, improving accuracy on reasoning tasks without requiring external tools. The technique leverages LLM temperature/sampling to generate diverse reasoning traces, then selects the most consistent answer across samples.","intents":["Improve LLM accuracy on reasoning tasks by sampling multiple solutions and voting","Understand how temperature and sampling parameters affect reasoning diversity","Implement self-consistency in applications where accuracy matters more than latency","Compare self-consistency with other reliability techniques like CoT and ensemble methods"],"best_for":["Teams building high-accuracy reasoning systems (math, logic, QA)","Applications where multiple LLM calls are acceptable for improved quality","Researchers studying LLM reliability and robustness","Developers optimizing accuracy-latency tradeoffs"],"limitations":["Requires multiple LLM API calls (typically 5-10x), significantly increasing cost and latency","Majority voting assumes answers are discrete/comparable; doesn't work well for open-ended generation","No guidance on optimal number of samples or aggregation strategies for different task types","Assumes LLM can generate diverse reasoning paths; may fail if model is deterministic or has limited reasoning variance"],"requires":["LLM API supporting temperature/sampling parameters","Computational budget for multiple inference calls","Ability to parse and compare LLM outputs for aggregation","Tasks with discrete or comparable answers"],"input_types":["Reasoning task prompts","Temperature/sampling parameters","Aggregation strategy specification"],"output_types":["Multiple reasoning traces from different samples","Aggregated answer with confidence score","Voting results showing answer distribution"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_6","uri":"capability://planning.reasoning.prompt.chaining.technique.for.decomposing.complex.tasks.into.sequential.steps","name":"prompt chaining technique for decomposing complex tasks into sequential steps","description":"Documents Prompt Chaining methodology that breaks complex tasks into sequential prompts where outputs from one step feed into the next, enabling task decomposition and intermediate validation. The guide explains how to design prompt chains, manage context between steps, and handle errors in multi-step workflows.","intents":["Break down complex tasks into manageable sequential steps that LLMs can handle better","Design workflows where each prompt builds on previous outputs","Implement error handling and validation between chain steps","Understand when prompt chaining is better than single complex prompts"],"best_for":["Developers building multi-step LLM workflows (content creation, analysis, code generation)","Teams implementing task decomposition for complex problems","Applications requiring intermediate validation or human review between steps","Builders optimizing for accuracy by breaking problems into smaller pieces"],"limitations":["Each chain step adds latency; no guidance on optimal chain length or parallelization strategies","Error propagation through chains can compound — early mistakes affect downstream steps","Context management between steps requires careful prompt engineering; no automated context optimization","Documentation doesn't address how to handle branching logic or conditional chains"],"requires":["Ability to design task decomposition strategies","LLM API for executing multiple sequential calls","Mechanism to pass outputs between chain steps","Error handling and retry logic"],"input_types":["Complex task descriptions","Intermediate outputs from previous chain steps","Validation criteria for step outputs"],"output_types":["Outputs from each chain step","Final aggregated result","Execution trace showing all intermediate steps"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_7","uri":"capability://planning.reasoning.automatic.prompt.engineer.ape.technique.for.optimizing.prompts.through.search","name":"automatic prompt engineer (ape) technique for optimizing prompts through search","description":"Documents Automatic Prompt Engineer methodology that uses LLMs to generate and optimize prompts for specific tasks through iterative search and evaluation. APE treats prompt optimization as a search problem, generating candidate prompts, evaluating them on a task, and iteratively improving based on performance feedback.","intents":["Automatically discover effective prompts for specific tasks without manual engineering","Understand how to set up prompt optimization pipelines with evaluation metrics","Learn the difference between zero-shot and few-shot APE variants","Apply APE to improve prompts for your own LLM applications"],"best_for":["Teams with large numbers of similar tasks needing prompt optimization","Researchers studying prompt optimization and meta-prompting","Developers wanting to reduce manual prompt engineering effort","Applications where small prompt improvements yield significant performance gains"],"limitations":["APE requires a good evaluation metric; if metric is poorly designed, optimization may not improve real performance","Computational cost is high — requires many LLM calls for generation and evaluation","Generated prompts may be verbose or difficult for humans to understand or modify","Documentation doesn't provide guidance on evaluation metric design or convergence criteria"],"requires":["Clear task definition with measurable evaluation metric","Training/validation dataset for evaluating candidate prompts","LLM API for prompt generation and task execution","Computational budget for iterative search"],"input_types":["Task description and examples","Evaluation metric specification","Search parameters (iterations, candidates per iteration)"],"output_types":["Optimized prompt discovered through search","Performance metrics showing improvement over baseline","Candidate prompts explored during search"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_8","uri":"capability://text.generation.language.zero.shot.and.few.shot.prompting.technique.documentation.with.examples","name":"zero-shot and few-shot prompting technique documentation with examples","description":"Explains foundational prompting techniques where zero-shot uses no examples and few-shot provides a small number of examples to guide LLM behavior. The guide documents how examples improve task understanding, the importance of example selection and ordering, and when zero-shot vs few-shot is appropriate.","intents":["Understand the difference between zero-shot and few-shot prompting and when to use each","Learn how to select and format examples for few-shot prompting","See how example quality and quantity affect LLM performance","Apply zero-shot and few-shot techniques to your own tasks"],"best_for":["Developers new to prompt engineering learning foundational concepts","Teams implementing basic LLM applications without fine-tuning","Researchers studying in-context learning and example effects","Builders optimizing prompt design for specific tasks"],"limitations":["Few-shot performance depends heavily on example selection; no automated example selection guidance provided","Limited discussion of how many examples are 'enough' — depends on task complexity and model size","Example ordering effects are mentioned but not thoroughly analyzed","Documentation doesn't address few-shot performance degradation with very large example sets"],"requires":["Understanding of basic LLM capabilities and limitations","Access to LLM API","For few-shot: labeled examples of the task"],"input_types":["Task description (zero-shot)","Task description + examples (few-shot)","Input instances to process"],"output_types":["LLM-generated outputs following the specified pattern","Performance metrics comparing zero-shot vs few-shot"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dair-ai--prompt-engineering-guide__cap_9","uri":"capability://code.generation.editing.program.aided.language.models.pal.for.code.based.reasoning.and.computation","name":"program-aided language models (pal) for code-based reasoning and computation","description":"Documents Program-Aided Language Models technique where LLMs generate executable code (Python, etc.) to solve problems rather than reasoning purely in natural language. PAL leverages LLMs' code generation abilities to handle complex math, logic, and computation tasks by writing programs that can be executed for precise results.","intents":["Use LLMs to generate executable code for solving math and logic problems","Understand when code generation is better than natural language reasoning","Implement PAL for applications requiring precise computation","See examples of PAL applied to math word problems, symbolic reasoning, and algorithms"],"best_for":["Developers building math solvers, logic engines, and computational tools","Teams solving problems requiring precise arithmetic or symbolic manipulation","Researchers studying LLM code generation and reasoning capabilities","Applications where execution correctness is critical"],"limitations":["Requires safe code execution environment; running arbitrary LLM-generated code is a security risk","LLM-generated code may have syntax errors or logical bugs; requires validation and error handling","Limited to problems that can be expressed as executable code; 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