prompt-engineering-patterns

Design LLM system prompts by establishing role-based constraints and validation rules to ensure reliable behavior. This approach builds modular, repeatable instructions that control model outputs and enforce safety in production settings.

Structure LLM outputs using JSON schemas or Pydantic models to ensure reliable parsing. This enforces parseable, validated results and controllable responses from the language model in production environments.

Chain-of-thought prompting improves complex reasoning by eliciting structured, verifiable steps from the LLM. This pattern breaks down intricate problems into sequential logic, maximizing performance and output traceability.

Improve LLM output consistency by strategically selecting and arranging few-shot learning examples within the prompt. This pattern guides the model to replicate desired formatting, quality, and reasoning behavior.

Yes, you can use modular template systems featuring variable interpolation and conditional logic for LLM prompts. This infrastructure ensures repeatability and modularity across complex prompt engineering workflows.

Yes, numpy is required as an environmental dependency to run the scripts and evaluation infrastructure. It supports the underlying computations needed for prompt optimization, A/B testing, and performance measurement.