prompt-engineering

To structure LLM prompts for automated agents, apply actionable patterns like few-shot learning, chain-of-thought prompting, and template systems to maximize reliability, controllability, and output quality across diverse tasks.

Create a reusable prompt template by defining system prompts and applying structured patterns that instruct the assistant to analyze pull requests, apply a reviewer checklist, and output structured feedback consistently.

Chain-of-thought prompting improves LLM output quality by guiding the model through intermediate reasoning steps, which enhances reliability and controllability for complex tasks performed by agents and sub-agents.

Common pitfalls when designing prompts for sub-agents include lacking reproducibility and failing to use structured patterns like few-shot learning or system prompts, which this skill addresses through established best practices.

Yes, you can use few-shot learning to optimize prompts by providing specific examples within the prompt, ensuring consistent results and reliable outputs across diverse tasks for LLM-based workflows.