From 5ec423ed9f2704e37eb92bd282eacb62fe690b3a Mon Sep 17 00:00:00 2001 From: Bap <83458751+fernandezbaptiste@users.noreply.github.com> Date: Wed, 8 Apr 2026 19:26:02 +0100 Subject: [PATCH] improve prompt-engineering-patterns skill description and structure - rewrite frontmatter description with specific actions and trigger terms - replace When to Use section with actionable prompt iteration workflow - remove Core Capabilities section (generic knowledge Claude already has) - remove Best Practices, Common Pitfalls, and Success Metrics sections --- .../prompt-engineering-patterns/SKILL.md | 99 ++----------------- 1 file changed, 9 insertions(+), 90 deletions(-) diff --git a/skills/ai-ml/prompt-engineering-patterns/SKILL.md b/skills/ai-ml/prompt-engineering-patterns/SKILL.md index 7a22910..804c8dc 100644 --- a/skills/ai-ml/prompt-engineering-patterns/SKILL.md +++ b/skills/ai-ml/prompt-engineering-patterns/SKILL.md @@ -1,71 +1,21 @@ --- name: prompt-engineering-patterns -description: Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates. +description: Design system prompts, structure few-shot examples, implement chain-of-thought reasoning, enforce structured outputs with Pydantic, and optimize token usage for production LLM applications. Use when writing a system prompt, building few-shot examples, adding chain of thought, tuning prompt performance, designing prompt templates, using JSON mode, or debugging inconsistent LLM outputs. --- # Prompt Engineering Patterns -Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. +## Prompt Iteration Workflow -## When to Use This Skill +Follow this sequence when designing or improving prompts: -- Designing complex prompts for production LLM applications -- Optimizing prompt performance and consistency -- Implementing structured reasoning patterns (chain-of-thought, tree-of-thought) -- Building few-shot learning systems with dynamic example selection -- Creating reusable prompt templates with variable interpolation -- Debugging and refining prompts that produce inconsistent outputs -- Implementing system prompts for specialized AI assistants -- Using structured outputs (JSON mode) for reliable parsing +1. **Start simple** - Write a direct instruction with no examples or constraints +2. **Test on edge cases** - Run the prompt against 5-10 diverse inputs including boundary cases +3. **Add constraints or examples** - If outputs are inconsistent, add format constraints or few-shot examples +4. **Enforce output schema** - Use Pydantic structured output for any prompt that feeds downstream code +5. **Measure and iterate** - Compare accuracy, consistency, and token usage across prompt versions -## Core Capabilities - -### 1. Few-Shot Learning - -- Example selection strategies (semantic similarity, diversity sampling) -- Balancing example count with context window constraints -- Constructing effective demonstrations with input-output pairs -- Dynamic example retrieval from knowledge bases -- Handling edge cases through strategic example selection - -### 2. Chain-of-Thought Prompting - -- Step-by-step reasoning elicitation -- Zero-shot CoT with "Let's think step by step" -- Few-shot CoT with reasoning traces -- Self-consistency techniques (sampling multiple reasoning paths) -- Verification and validation steps - -### 3. Structured Outputs - -- JSON mode for reliable parsing -- Pydantic schema enforcement -- Type-safe response handling -- Error handling for malformed outputs - -### 4. Prompt Optimization - -- Iterative refinement workflows -- A/B testing prompt variations -- Measuring prompt performance metrics (accuracy, consistency, latency) -- Reducing token usage while maintaining quality -- Handling edge cases and failure modes - -### 5. Template Systems - -- Variable interpolation and formatting -- Conditional prompt sections -- Multi-turn conversation templates -- Role-based prompt composition -- Modular prompt components - -### 6. System Prompt Design - -- Setting model behavior and constraints -- Defining output formats and structure -- Establishing role and expertise -- Safety guidelines and content policies -- Context setting and background information +Move to the next level only when the current level fails. Simpler prompts are cheaper, faster, and easier to maintain. ## Quick Start @@ -440,34 +390,3 @@ response = client.messages.create( ) ``` -## Best Practices - -1. **Be Specific**: Vague prompts produce inconsistent results -2. **Show, Don't Tell**: Examples are more effective than descriptions -3. **Use Structured Outputs**: Enforce schemas with Pydantic for reliability -4. **Test Extensively**: Evaluate on diverse, representative inputs -5. **Iterate Rapidly**: Small changes can have large impacts -6. **Monitor Performance**: Track metrics in production -7. **Version Control**: Treat prompts as code with proper versioning -8. **Document Intent**: Explain why prompts are structured as they are - -## Common Pitfalls - -- **Over-engineering**: Starting with complex prompts before trying simple ones -- **Example pollution**: Using examples that don't match the target task -- **Context overflow**: Exceeding token limits with excessive examples -- **Ambiguous instructions**: Leaving room for multiple interpretations -- **Ignoring edge cases**: Not testing on unusual or boundary inputs -- **No error handling**: Assuming outputs will always be well-formed -- **Hardcoded values**: Not parameterizing prompts for reuse - -## Success Metrics - -Track these KPIs for your prompts: - -- **Accuracy**: Correctness of outputs -- **Consistency**: Reproducibility across similar inputs -- **Latency**: Response time (P50, P95, P99) -- **Token Usage**: Average tokens per request -- **Success Rate**: Percentage of valid, parseable outputs -- **User Satisfaction**: Ratings and feedback