prompt-engineering-patterns

Optimize LLM prompts by applying chain-of-thought reasoning, few-shot learning, and modular templates. This Skill provides iterative refinement, A/B testing frameworks, and performance measurement to ensure reliability and measurable accuracy across complex tasks.

Structure few-shot examples using semantic similarity, diversity sampling, difficulty-based selection, and error-based selection. These strategies optimize the examples provided to the LLM, improving accuracy and reliability for specialized tasks.

Create reusable system prompts by designing role-based, safety-conscious instructions using modular templates with variable interpolation and conditional sections. This approach ensures controllability and safety across specialized assistant workflows.

Yes, you can apply prompt optimization frameworks that include A/B testing and iterative refinement. This allows you to measure performance, validate outputs, and debug prompts systematically in production environments.

Chain-of-thought prompting improves accuracy by eliciting structured, step-by-step reasoning from the LLM. Breaking down complex problems into sequential logic reduces errors and enhances reliability for multi-step tasks.

Template-based prompts require rigorous validation, testing, and documentation to maintain reliability. Without iterative refinement and safety checks, modular templates may fail to handle edge cases or ensure measurable performance in production.