customaize-agent:prompt-engineering

Advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

Teach the model by showing examples instead of explaining rules. Include 2-5 input-output pairs that demonstrate the desired behavior. Use when you need consistent formatting, specific reasoning patterns, or handling of edge cases. More examples improve accuracy but consume tokens—balance based on task complexity.

Request step-by-step reasoning before the final answer. Add "Let's think step by step" (zero-shot) or include example reasoning traces (few-shot). Use for complex problems requiring multi-step logic, mathematical reasoning, or when you need to verify the model's thought process. Improves accuracy on analytical tasks by 30-50%.

当您为代理编写命令、挂钩、技能或子代理或任何其他 LLM 交互的提示时，请使用此技能，包括优化提示、改进 LLM 输出或设计生产提示模板。 来源：neolabhq/context-engineering-kit。