Prompt engineering is the craft of phrasing a request so a model does what you actually want. The same model can produce a vague ramble or a crisp, structured answer depending entirely on how the instruction is written. Because the model only sees your words, small wording changes can swing the output sharply. Treating that text as something you iterate on, not something you write once, is the whole idea.
A few reliable moves cover most cases. Spell out the role and goal, show one or two worked examples, state the exact output format, and give the model room to reason step by step before it commits to an answer. Constraints like word limits or a required JSON shape reduce surprises downstream.
For founders, prompt engineering is the cheapest lever available. It needs no training runs, no infrastructure, and no data pipeline; you edit text and rerun, and the feedback loop is measured in seconds. Before spending on fine-tuning or a bigger model, it is almost always worth exhausting what careful prompting can achieve first.
The discipline shows up when prompts move into production. Treat them as versioned assets, test them against a fixed set of tricky inputs, and watch for prompt injection, where user text tries to override your instructions. A prompt that works in a demo can still leak or misbehave once real users start typing.