prompt-engineering-patterns

Structured JSON outputs are enforced using Pydantic schemas and verification strategies to ensure type-safe, parseable results. This prevents brittle responses by validating fields dynamically before returning them to production workflows.

Few-shot example selection uses semantic retrieval via embeddings, diversity sampling, and difficulty-based curation. This approach dynamically matches examples to the input query, improving response accuracy and consistency.

Chain-of-thought and self-consistency are reasoning patterns applied for complex multi-step tasks. They generate multiple reasoning paths and aggregate answers to improve controllability and reliability.

Numpy supports the underlying mathematical operations for dynamic example retrieval via embeddings. It enables semantic selection and diversity sampling to curate optimal few-shot prompts.

Token-budget management and metric tracking are handled through automation scripts for A/B testing. These scripts monitor accuracy, latency, and token use to iteratively refine prompts.

Template systems with conditional sections and role-based system prompts are used for specialized assistants. They provide modular, repeatable structures that reduce inconsistent outputs in production environments.