Prompt engineering is the practice of crafting effective input prompts to large language models to elicit desired outputs. It encompasses techniques like clear instructions, few-shot examples, structured output specifications, chain-of-thought reasoning, role assignments, context provision, and iterative refinement. The discipline is part craft (intuition for what works) and part science (testable techniques), and is the dominant way to control LLM behavior without fine-tuning. It's the AI-era equivalent of writing good SQL queries: a transferable skill that materially impacts the quality of what you can build.
The core techniques that work:
Clear, specific instructions: vague prompts produce vague outputs.
Bad: "Summariz...