prompt-engineering-patterns

Design reliable LLM prompts by applying structured patterns like chain-of-thought and few-shot. This approach uses production-ready templates, dynamic example selection, and robust validation to ensure controllable and verifiable model outputs.

The best way to structure LLM prompts for verifiable results is using system-prompt strategies with structured outputs. This ensures safe, controllable interactions by enforcing robust validation and production-ready templates across complex prompt designs.

Yes, you can use prompt engineering templates for dynamic example selection in production. The skill provides production-ready templates that emphasize structured outputs and robust evaluation to maintain reliable LLM behavior across varying inputs.

Chain-of-thought prompt engineering improves LLM output reliability by guiding the model through structured reasoning steps. This pattern, combined with robust validation, ensures complex prompt designs yield safe, verifiable, and controllable results.

You need numpy installed to support the underlying evaluation and validation logic for complex prompt designs. This dependency helps facilitate robust verification and dynamic example selection within the prompt engineering workflow.