Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.

Prompt Engineering Patterns Advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. Core Capabilities 1. Few Shot Learning Teach the model by showing examples instead of explaining rules. Include 2 5 input output pairs that demonstrate the desired behavior. Use when you need consistent formatting, specific reasoning patterns, or handling of edge cases. More examples improve accuracy but consume tokens—balance based on task complexity. Example: 2. Chain of Thought Prompting Request step by step reasoning before the final answer. Add "Let's think step by step" (zero shot) or include example reasoning traces (few shot). Use for complex problems requiring multi step logic, mathematical reasoning, or when you need to verify the model's thought process. Improves accuracy on analytical tasks by 30 50%. Example: 3. Prompt Optimization Systematically improve prompts through testing and refinement. Start simple, measure performance (accuracy, consistency, token usage), then iterate. Test on diverse inputs including edge cases. Use A/B testing to compare variations. Critical for production prompts where consistency and cost matter. Example: 4. Template Systems Build reusable prompt structures with variables, conditional sections, and modular components. Use for multi turn conversations, role based interactions, or when the same pattern applies to different inputs. Reduces duplication and ensures consistency across similar tasks. Example: 5. System Prompt Design Set global behavior and constraints that persist across the conversation. Define the model's role, expertise level, output format, and safety guidelines. Use system prompts for stable instructions that shouldn't change turn to turn, freeing up user message tokens for variable content. Example: Key Patterns Progressive Disclosure Start with simple prompts, add complexity only when needed: 1. Level 1 : Direct instruction "Summarize this article" 2. Level 2 : Add constraints "Summarize this article in 3 bullet points, focusing on key findings" 3. Level 3 : Add reasoning "Read this article, identify the main findings, then summarize in 3 bullet points" 4. Level 4 : Add examples Include 2 3 example summaries with input output pairs Instruction Hierarchy Error Recovery Build prompts that gracefully handle failures: Include fallback instructions Request confidence scores Ask for alternative interpretations when uncertain Specify how to indicate missing information Best Practices 1. Be Specific : Vague prompts produce inconsistent results 2. Show, Don't Tell : Examples are more effective than descriptions 3. Test Extensively : Evaluate on diverse, representative inputs 4. Iterate Rapidly : Small changes can have large impacts 5. Monitor Performance : Track metrics in production 6. Version Control : Treat prompts as code with proper versioning 7. 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 When to Use This skill is applicable to execute the workflow or actions described in the overview. Limitations Use this skill only when the task clearly matches the scope described above. Do not treat the output as a substitute for environment specific validation, testing, or expert review. Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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