prompt-engineering-patterns

Prompt-engineering patterns provide reusable template structures—few-shot examples, chain-of-thought reasoning, and verification steps—that generalize across models and domains, reducing trial-and-error and improving output reliability in production.

Few-shot prompt templates show the model input-output examples before the actual task, establishing patterns for reasoning and formatting. This skill provides curated templates and best practices to construct these patterns effectively for your specific domain.

Chain-of-thought prompting guides LLMs through step-by-step reasoning before producing a final answer, improving accuracy on complex tasks. This skill includes structured templates and verification workflows to implement and validate chain-of-thought patterns.

Yes. This skill covers production-oriented patterns that work with RAG systems, including template-driven design that combines retrieval results with optimized prompt structures for downstream LLM processing.

The skill's reference library documents common pitfalls—ambiguous instructions, insufficient examples, missing verification—and provides best practices to prevent them, accelerating deployment and reducing debugging cycles.