Prompt engineering is the set of techniques for writing and structuring the instructions given to a generative AI model, to get high-quality, reliable answers that match the expected behavior.
It relies on proven practices: giving examples (few-shot), asking the model to reason step by step, enforcing an output format (JSON for example), separating system instructions from user messages, providing the right context and planning for failure cases ("if the information is missing, say so").
With agents, the scope widens: describing the tools the model can call, managing what goes into the context window, chaining several steps. The quality of an AI application often depends as much on the instructions as on the chosen model.
Why it matters when hiring
It has become a cross-functional skill, expected from AI Engineers but also from Product Managers and domain experts. To tell real skill apart, ask the candidate to fix a prompt that produces errors: a strong profile starts by identifying the failing cases and tests their changes, instead of rewriting by gut feeling.