Prompt engineering is the art of communicating effectively with AI models. Small changes in phrasing can dramatically change outputs.
Basic techniques: be specific and clear, provide context, use examples (few-shot prompting), and specify output format.
Chain-of-thought: ask the model to reason step by step before answering. "Think through this problem: ..." dramatically improves accuracy for complex tasks.
System prompts set behavior, personality, and constraints. Role assignment: You are an expert in.... Format specification: Respond in JSON, bullet points, or markdown.
Advanced patterns: tree of thoughts (explore multiple reasoning paths), self-consistency (generate multiple answers and vote), and retrieval-augmented generation (RAG) for factual accuracy.
Common mistakes: being too vague, not providing enough context, ignoring model limitations, and not iterating on prompts.
Prompt engineering is a skill that improves with practice. Document what works, share templates, and continuously refine.