What is Prompt engineering?

The practice of crafting and refining prompts to get the best results from an AI model, including examples, formatting and step-by-step instructions.

Prompt engineering is the practice of writing and iterating on prompts to get reliable, high-quality output from a model. It is less mysterious than the name suggests. The core techniques are simple: state the task and desired format explicitly, give the model a role or audience, include a few worked examples (few-shot prompting), ask it to reason step by step for complex problems, and specify what to do when information is missing. For repeated or production use, it also means testing prompt variations against real inputs and keeping the version that performs best. Techniques transfer only partially between models; a prompt tuned for one model may need adjusting for another. As models improve at following plain instructions, elaborate prompt tricks matter less, but clear task specification still matters a lot.

Example

A support team improves an email-drafting prompt by adding three example replies in their brand voice and a rule to never promise refunds. Draft quality jumps without changing the model.

Why it matters

Before paying for a bigger model or fine-tuning, better prompting is usually the cheapest way to improve results, and it is a skill that carries across tools. Browse the AI tools directory or the model leaderboard to put it into practice.