prompt-engineering-patterns

Structured LLM output is achieved through schema enforcement using JSON and Pydantic. This approach ensures type-safe parsing and validation, allowing downstream automation pipelines to reliably consume the generated responses without parsing errors.

Few-shot example selection for prompt engineering should prioritize relevance and diversity. By strategically choosing examples that cover various scenarios, you improve task-specific performance and model consistency for complex operations like sentiment analysis or SQL generation.

Multi-step reasoning in large language models is improved by applying chain-of-thought and self-consistency approaches. These prompt engineering techniques elicit reliable, step-by-step logical deductions, significantly enhancing accuracy for complex problem-solving tasks.

Prompt optimization workflows use metrics-driven iteration and A/B testing to maximize token efficiency. By systematically measuring accuracy, consistency, and latency requirements, you can refine production prompt templates to achieve reliable performance at scale.

Yes, this approach supports RAG-based workflows through modular template systems and system prompt design. It enables role-based multi-turn assistants to integrate retrieval-augmented generation for tasks like question answering and natural language to SQL conversion.

Yes, numpy is required as a dependency for these prompt optimization scripts. It provides the numerical computation backend necessary for executing A/B testing metrics and evaluating prompt performance data during iteration.