prompt-engineering-patterns

Production-ready LLM prompts use structured template systems with variable interpolation and few-shot example selection to ensure reliable classification and extraction. This skill provides optimization workflows with iterative refinement and A/B testing metrics to maximize maintainability.

Implement few-shot prompting by using a FewShotSelector to choose relevant demonstrations based on semantic similarity and diversity sampling. This skill provides tooling to assemble system instructions and task examples, rendering optimized prompts for execution.

Chain-of-thought prompting improves LLM reasoning traces by structuring prompts to guide step-by-step logical deduction. This skill provides chain-of-thought and tree-of-thought reasoning patterns to enhance model performance on complex production tasks.

Prompt templates support modular variable interpolation within system prompts to ensure reusable and maintainable designs. This skill provides a PromptTemplate component to assemble system instructions, task instructions, and demonstrations dynamically.

Numpy is required to run the prompt optimization and evaluation workflows within this skill. It provides the foundational numerical operations required for semantic similarity calculations and few-shot diversity sampling.

Integrate RAG and validation patterns with system prompt design by applying systematic integration patterns for retrieval and monitoring. This skill provides tooling to connect prompt templates with validation logic ensuring controllability in production environments.