prompt-engineering-patterns

Few-shot learning improves LLM accuracy by providing structured examples that guide model behavior. Select diverse, representative examples, refine them iteratively through A/B testing, and reduce tokens to cut costs while maintaining performance on your specific task.

Chain-of-thought prompting elicits step-by-step reasoning from LLMs by asking them to show their work before providing answers. Use it for logic, math, multi-step problem-solving, and complex troubleshooting where intermediate reasoning dramatically improves accuracy.

System prompt design defines your LLM's role, expertise, constraints, and output formats to ensure predictable behavior. Specify constraints clearly, establish desired output formats, and lock in safety guidelines so the model operates consistently within your requirements.

Yes. This Skill supports interoperability with retrieval pipelines, enabling dynamic example selection based on retrieved context, system prompts tailored to RAG workflows, and prompt templates that incorporate structured retrieval results for enhanced accuracy.

No. Prompt engineering focuses on crafting effective instructions and examples rather than building models. Dependencies like scikit-learn and sentence-transformers handle similarity computations, but the core task—refining prompts through structured patterns—requires domain knowledge, not ML expertise.

System prompts define global role and constraints applied to all interactions, while few-shot examples demonstrate desired behavior through specific input-output pairs. Together they establish context and guide the model toward consistent, high-quality outputs.