prompt-engineering-patterns

To create reusable prompt templates, you define pattern discovery and few-shot templates with explicit frontmatter metadata. This enforces structured outputs and consistent results for production LLMs across tasks like QA and classification.

The best way to enforce structured JSON outputs is by using prompt patterns that mandate explicit frontmatter metadata and structured output templates. This enforces consistent formatting and technical requirements for your LLM responses.

A/B testing for prompt engineering works by applying robust testing and verification workflows to different prompt patterns. It evaluates template variations to improve reliability and optimize performance across tasks like code generation and classification.

Yes, you need numpy installed because it is a required dependency for running the prompt engineering pattern discovery, validation, and A/B testing workflows. It supports the underlying evaluation framework calculations.

You should use few-shot templates instead of zero-shot prompts when you need to enforce consistent results and structured outputs for production LLMs. Few-shot templates provide scalable pattern design for tasks like QA and code generation.

Available prompt patterns for code generation and classification include template libraries with few-shot examples, explicit frontmatter metadata, and structured outputs. These patterns enforce safety checks and robust testing for scalable design.