Prompt engineering is the practice of designing, formulating, and refining the instructions (the prompts) sent to a language model to guide its behavior and obtain more accurate, useful responses tailored to a specific objective. It is not just about "asking well," but about structuring the context, the expected format, the tone, and the constraints so that the model clearly understands what is being requested.
It matters because the quality of the output depends largely on how the input is framed: the same model can provide mediocre or excellent results depending on how it is instructed. Some common techniques include:
- Assigning a role ("act as a legal reviewer").
- Providing examples within the prompt itself (few-shot).
- Asking for intermediate steps to encourage reasoning.
- Specifying the output format (table, JSON, list).
A practical nuance: instead of writing "summarize this," something like "summarize the following text in three sentences aimed at a non-expert audience" usually works better. The more explicit and constrained the instruction, the more reliable the result will be.