The same question, asked two different ways, can produce a usable answer or a useless one. Prompt engineering is the discipline of closing that gap: giving the model clear role, format, constraints, and relevant examples instead of a vague one-line request, and testing that the same prompt holds up across edge cases.
It matters more in production than it looks like it should, because a prompt that works in a demo with one clean input often breaks against messy real-world inputs the demo never tested. Good prompt engineering treats the prompt like a specification, versioned and tested, not a one-off message.
It has real limits, though. A prompt can’t fix a model that’s missing the right information (that’s a retrieval problem) or a model that’s fundamentally the wrong shape for the task (that’s a fine-tuning or model-choice problem). Treating every quality issue as a prompting problem is one of the most common ways enterprise pilots stall.