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, not just the first example that worked.
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.