Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

Prompt Engineering Patterns Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. Do not use this skill when The task is unrelated to prompt engineering patterns You need a different domain or tool outside this scope Instructions Clarify goals, constraints, and required inputs. Apply relevant best practices and validate outcomes. Provide actionable steps and verification. If detailed examples are required, open resources/implementation playbook.md . Use this skill when 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 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. 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 4. Template Systems Variable interpolation and formatting Conditional prompt sections Multi turn conversation templates Role based prompt composition Modular prompt components 5. 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 Quick Start 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 Integration Patterns With RAG Systems With Validation Performance Optimization Token Efficiency Remove redundant words and phrases Use abbreviations consistently after first definition Consolidate similar instructions Move stable content to system prompts Latency Reduction Minimize prompt length without sacrificing quality Use streaming for long form outputs Cache common prompt prefixes Batch similar requests when possible Resources references/few shot learning.md : Deep dive on example selection and construction references/chain of thought.md : Advanced reasoning elicitation techniques references/prompt optimization.md : Systematic refinement workflows references/prompt templates.md : Reusable template patterns references/system prompts.md : System level prompt design assets/prompt template library.md : Battle tested prompt templates assets/few shot examples.json : Curated example datasets scripts/optimize prompt.py : Automated prompt optimization tool 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 outputs User Satisfaction : Ratings and feedback Next Steps 1. Review the prompt template library for common patterns 2. Experiment with few shot learning for your specific use case 3. Implement prompt versioning and A/B testing 4. Set up automated evaluation pipelines 5. Document your prompt engineering decisions and learnings 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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