prompt-engineering-patterns

Production prompt patterns are created by applying structured templates, few-shot learning, and chain-of-thought reasoning to maximize LLM performance and reliability in production environments.

The best way to structure few-shot learning and chain-of-thought prompts is by using pattern-based templates that enable structured reasoning, output consistency, and integration with validation workflows.

Yes, you can use prompt engineering patterns with RAG and validation workflows by applying structured prompt templates and verification steps to design robust, auditable AI behavior.

You can version and test prompt templates by applying structured prompt engineering techniques that enable templating, reuse, and verification steps to improve prompt quality and safety.

Numpy is listed as a dependency for applying these structured prompt templates and verification steps, suggesting it supports the underlying numerical or data processing logic for the patterns.