{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-openai-prompt-engineering-guide","slug":"openai-prompt-engineering-guide","name":"OpenAI Prompt Engineering Guide","type":"prompt","url":"https://platform.openai.com/docs/guides/prompt-engineering","page_url":"https://unfragile.ai/openai-prompt-engineering-guide","categories":["prompt-engineering"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-openai-prompt-engineering-guide__cap_0","uri":"capability://text.generation.language.structured.prompt.composition.with.role.based.context.framing","name":"structured prompt composition with role-based context framing","description":"Teaches developers to construct prompts by explicitly defining system roles, task context, and output constraints through a hierarchical structure. The approach uses role-based prefixing (e.g., 'You are a...') combined with clear task boundaries and example-driven formatting to reduce ambiguity and improve model adherence to intended behavior. This is implemented as a mental model and template pattern rather than code, enabling consistent prompt design across different LLM providers.","intents":["I need to write prompts that consistently produce the output format I expect","I want to reduce hallucinations and off-topic responses from my LLM calls","I need to teach my model to adopt a specific persona or expertise level for better results"],"best_for":["developers building LLM applications without fine-tuning budgets","teams standardizing prompt patterns across multiple models","non-technical builders prototyping LLM-powered features"],"limitations":["effectiveness varies significantly across model architectures and sizes — patterns that work for GPT-4 may fail on smaller open models","no programmatic validation of prompt quality — requires manual testing and iteration","role-based framing adds token overhead without guaranteed improvement on all task types"],"requires":["access to at least one LLM API (OpenAI, Anthropic, etc.)","understanding of natural language and task decomposition","iterative testing capability to validate prompt effectiveness"],"input_types":["natural language task descriptions","example input-output pairs","constraint specifications"],"output_types":["structured text","JSON","code","markdown"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openai-prompt-engineering-guide__cap_1","uri":"capability://text.generation.language.few.shot.example.injection.for.task.specification","name":"few-shot example injection for task specification","description":"Demonstrates how to embed concrete input-output examples directly in prompts to teach models task behavior through demonstration rather than explicit instruction. The technique works by placing 2-5 representative examples before the actual task, leveraging the model's in-context learning to infer patterns and apply them to new inputs. This is a zero-cost alternative to fine-tuning that exploits the model's ability to recognize and generalize from patterns in the prompt context window.","intents":["I want my model to learn a task format from examples without fine-tuning","I need consistent output formatting for downstream processing","I'm working with a niche task that's hard to describe in natural language"],"best_for":["rapid prototyping teams with tight iteration cycles","builders working with proprietary or domain-specific tasks","developers optimizing for latency (examples are faster than fine-tuning)"],"limitations":["example quality directly impacts output quality — poor examples degrade performance more than poor instructions","context window limits the number of examples (typically 2-5 before diminishing returns or token exhaustion)","inconsistent behavior across model sizes — GPT-4 generalizes better from fewer examples than GPT-3.5","no mechanism to weight or prioritize examples — all examples treated equally"],"requires":["representative examples of the task (input-output pairs)","understanding of the model's context window size","ability to format examples consistently"],"input_types":["natural language examples","structured data examples","code examples"],"output_types":["text","structured data","code"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openai-prompt-engineering-guide__cap_2","uri":"capability://planning.reasoning.chain.of.thought.reasoning.elicitation.through.prompt.structuring","name":"chain-of-thought reasoning elicitation through prompt structuring","description":"Teaches developers to explicitly request step-by-step reasoning in prompts using phrases like 'think step by step' or 'explain your reasoning', which triggers the model to generate intermediate reasoning tokens before producing final answers. This approach leverages the model's ability to use its own generated text as context for refinement, effectively creating a multi-step reasoning process within a single forward pass. The technique is implemented as a prompt template pattern that can be combined with other strategies like role-framing and examples.","intents":["I need my model to show its work and be more transparent about how it arrived at answers","I want to reduce errors on complex reasoning tasks like math or logic puzzles","I need to debug why my model is producing incorrect outputs"],"best_for":["developers building reasoning-heavy applications (math, logic, analysis)","teams needing explainability for compliance or debugging","builders working with smaller models that benefit from explicit reasoning scaffolding"],"limitations":["increases token consumption by 2-5x due to intermediate reasoning generation","not all task types benefit — simple classification or retrieval tasks may see no improvement","reasoning quality degrades on tasks outside the model's training distribution","no guarantee that intermediate reasoning is correct — models can 'reason' to wrong conclusions"],"requires":["LLM with sufficient context window to accommodate reasoning tokens","task that benefits from multi-step reasoning","tolerance for increased latency and token costs"],"input_types":["natural language questions","math problems","logic puzzles","analysis tasks"],"output_types":["text with reasoning steps","structured reasoning traces","final answers with justification"],"categories":["planning-reasoning","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openai-prompt-engineering-guide__cap_3","uri":"capability://text.generation.language.output.format.specification.and.constraint.enforcement","name":"output format specification and constraint enforcement","description":"Provides patterns for explicitly specifying desired output formats (JSON, XML, markdown, code) and constraints (length limits, field requirements, value ranges) directly in prompts. The approach uses natural language constraints combined with format examples to guide model generation toward structured outputs that can be reliably parsed downstream. This is implemented as a template pattern that combines role-framing, examples, and explicit format instructions to reduce parsing failures and validation errors.","intents":["I need my LLM output to be parseable as JSON or structured data","I want to enforce constraints like maximum length or required fields without post-processing","I need to reduce the number of malformed outputs that break my downstream pipeline"],"best_for":["developers building LLM-powered APIs that need structured responses","teams integrating LLM outputs into automated workflows","builders working with models that don't support function calling or structured output modes"],"limitations":["models occasionally violate format constraints despite explicit instructions — no guarantee of compliance","complex nested structures are harder to specify and more prone to errors than simple formats","format specification adds prompt tokens and can reduce model's ability to focus on task content","no built-in validation — requires downstream parsing and error handling"],"requires":["clear specification of desired output format","examples of correctly formatted outputs","parsing logic to handle occasional format violations"],"input_types":["natural language task descriptions","format specifications (JSON schema, XML structure, etc.)","constraint definitions"],"output_types":["JSON","XML","CSV","markdown","code"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openai-prompt-engineering-guide__cap_4","uri":"capability://planning.reasoning.iterative.prompt.refinement.through.systematic.testing","name":"iterative prompt refinement through systematic testing","description":"Teaches a methodology for evaluating and improving prompts through systematic testing against representative examples, measuring performance metrics, and iterating on prompt components. The approach involves defining success criteria, testing prompts against a small evaluation set, analyzing failure modes, and adjusting prompt elements (role, examples, constraints) based on results. This is implemented as a mental model and workflow pattern rather than automated tooling, requiring manual evaluation and iteration.","intents":["I want to know if my prompt changes actually improve performance","I need to debug why my prompt is failing on certain inputs","I want to optimize my prompt before deploying to production"],"best_for":["teams with time to invest in prompt optimization","developers building production LLM systems where quality matters","builders working with limited budgets who can't afford fine-tuning"],"limitations":["manual testing is time-consuming and doesn't scale to large prompt spaces","small evaluation sets may not catch edge cases or distribution shifts","no programmatic way to search the prompt space — requires human intuition and trial-and-error","results are model-specific and may not transfer to other models or versions"],"requires":["representative evaluation examples","clear success criteria or metrics","ability to run multiple prompt variations","time for iterative testing"],"input_types":["test cases","evaluation metrics","prompt variations"],"output_types":["performance reports","failure analysis","optimized prompts"],"categories":["planning-reasoning","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openai-prompt-engineering-guide__cap_5","uri":"capability://planning.reasoning.model.capability.matching.and.task.to.model.alignment","name":"model capability matching and task-to-model alignment","description":"Provides guidance on selecting appropriate models for specific tasks based on capability profiles (reasoning, coding, language understanding, etc.) and understanding when to use simpler vs. more capable models. The approach involves analyzing task requirements, understanding model strengths and weaknesses, and making cost-performance tradeoffs. This is implemented as a knowledge base and decision framework rather than automated tooling, requiring human judgment to apply.","intents":["I need to choose between GPT-4 and GPT-3.5 for my use case","I want to understand which model is best suited for my task","I need to optimize costs by using the simplest model that works"],"best_for":["developers building LLM applications with cost constraints","teams evaluating multiple models for a specific task","builders new to LLMs who need guidance on model selection"],"limitations":["model capabilities change with updates — guidance becomes stale","task-to-model matching is heuristic-based, not data-driven","no automated testing framework to validate model suitability","guidance is specific to OpenAI models and may not apply to other providers"],"requires":["understanding of task requirements","access to multiple models for comparison","ability to run benchmarks or evaluations"],"input_types":["task descriptions","performance requirements","cost constraints"],"output_types":["model recommendations","capability comparisons","cost-performance analysis"],"categories":["planning-reasoning","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openai-prompt-engineering-guide__cap_6","uri":"capability://text.generation.language.common.pitfall.avoidance.and.anti.pattern.identification","name":"common pitfall avoidance and anti-pattern identification","description":"Teaches developers to recognize and avoid common prompt engineering mistakes (e.g., unclear instructions, contradictory constraints, over-specification) that degrade model performance. The approach involves documenting failure modes, explaining why they occur, and providing corrected examples. This is implemented as a knowledge base of anti-patterns with explanations and fixes, enabling developers to self-correct during prompt design.","intents":["I want to understand why my prompt isn't working","I need to avoid common mistakes when writing prompts","I want to learn from others' failures to improve my own prompts"],"best_for":["developers new to prompt engineering","teams standardizing prompt practices","builders debugging failing prompts"],"limitations":["anti-patterns are heuristic-based and may not apply to all models or tasks","no automated detection of anti-patterns in user prompts","guidance is specific to OpenAI models","new anti-patterns emerge as models and use cases evolve"],"requires":["understanding of prompt structure and model behavior","willingness to refactor existing prompts"],"input_types":["problematic prompts","failure examples"],"output_types":["corrected prompts","explanations of failures","best practices"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-openai-prompt-engineering-guide__cap_7","uri":"capability://planning.reasoning.prompt.composition.strategy.selection.and.technique.combination","name":"prompt composition strategy selection and technique combination","description":"Provides guidance on selecting and combining multiple prompt engineering techniques (role-framing, few-shot examples, chain-of-thought, constraints) based on task characteristics and constraints. The approach involves analyzing task complexity, available resources (tokens, latency), and model capabilities to recommend a composition strategy. This is implemented as a decision framework and set of templates that show how to combine techniques effectively.","intents":["I need to combine multiple prompt techniques for a complex task","I want to know which techniques to use together and which to avoid","I need to balance quality, latency, and cost in my prompt design"],"best_for":["developers building complex LLM applications","teams optimizing prompts for production performance","builders working with tight constraints (latency, tokens, cost)"],"limitations":["technique interactions are complex and not fully understood — some combinations work better than others but patterns are heuristic","no automated framework for selecting optimal technique combinations","guidance is specific to OpenAI models and may not transfer","tradeoffs between quality, latency, and cost are task-dependent"],"requires":["understanding of individual prompt techniques","clear task requirements and constraints","ability to measure and compare prompt performance"],"input_types":["task descriptions","performance requirements","constraint specifications"],"output_types":["prompt composition strategies","technique recommendations","template prompts"],"categories":["planning-reasoning","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["access to at least one LLM API (OpenAI, Anthropic, etc.)","understanding of natural language and task decomposition","iterative testing capability to validate prompt effectiveness","representative examples of the task (input-output pairs)","understanding of the model's context window size","ability to format examples consistently","LLM with sufficient context window to accommodate reasoning tokens","task that benefits from multi-step reasoning","tolerance for increased latency and token costs","clear specification of desired output format"],"failure_modes":["effectiveness varies significantly across model architectures and sizes — patterns that work for GPT-4 may fail on smaller open models","no programmatic validation of prompt quality — requires manual testing and iteration","role-based framing adds token overhead without guaranteed improvement on all task types","example quality directly impacts output quality — poor examples degrade performance more than poor instructions","context window limits the number of examples (typically 2-5 before diminishing returns or token exhaustion)","inconsistent behavior across model sizes — GPT-4 generalizes better from fewer examples than GPT-3.5","no mechanism to weight or prioritize examples — all examples treated equally","increases token consumption by 2-5x due to intermediate reasoning generation","not all task types benefit — simple classification or retrieval tasks may see no improvement","reasoning quality degrades on tasks outside the model's training distribution","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.15,"quality":0.25,"ecosystem":0.1,"match_graph":0.45,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.579Z","last_scraped_at":"2026-05-03T14:00:20.516Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=openai-prompt-engineering-guide","compare_url":"https://unfragile.ai/compare?artifact=openai-prompt-engineering-guide"}},"signature":"zzywyEoncEzBIMVl2q2/3Fyv3F4ZMMUnJZ978jR3NFmCrr8AMNAeiM4g18+hoAfE70zRtmw4TOTDwvEUf4bIAQ==","signedAt":"2026-06-20T06:56:53.503Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/openai-prompt-engineering-guide","artifact":"https://unfragile.ai/openai-prompt-engineering-guide","verify":"https://unfragile.ai/api/v1/verify?slug=openai-prompt-engineering-guide","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}