prompt-engineering-patterns

Production-ready prompts are designed using a structured workflow that applies few-shot templates and chain-of-thought patterns to standardize LLM interactions. This approach improves model performance and controllability by providing explicit reasoning steps and task examples.

The best way to measure prompt accuracy and consistency is using built-in evaluation and validation workflows. These workflows test prompts against metrics like latency and accuracy to ensure robust, auditable results before deploying to production.

Verification steps and safety guardrails are needed when generating complex outputs like code or data extraction. They ensure reliable, auditable results by validating the LLM's reasoning process and preventing unsafe or inconsistent model behaviors.

Yes, you can reuse and version prompt structures across tasks using a pattern library. This library bundles system, task, and example components to standardize interactions, ensuring consistent versioning and evaluation metrics across different LLM workflows.

Yes, numpy is required as a dependency to run the prompt optimization workflow. It supports the underlying computational logic needed to process evaluation metrics, validation workflows, and structured templates within the pattern library.