prompt-engineering-patterns

To achieve reliable LLM outputs, combine few-shot examples with chain-of-thought patterns to guide reasoning and structure generation. This approach reduces guesswork and errors in production contexts by providing actionable templates for diverse tasks.

The best way to enforce structured output is applying structured output patterns alongside system prompt design. This Skill illustrates combining these techniques to improve LLM controllability and reliability for production-grade applications.

Testing and validating prompt engineering patterns involves conforming to best practices for verification and error recovery. This Skill provides actionable guidance on prompt versioning, validation, and documentation for development teams.

Yes, you can use these prompt engineering templates for data extraction and RAG workflows. The Skill offers quick-start templates and best practices specifically across data, software, and content workflows including RAG verification and error recovery.

Yes, you need numpy installed as a dependency to run the prompt-engineering-patterns scripts and examples. The Skill components include scripts and assets that rely on this environment setup for execution.