What it means
Prompt engineering is the craft of structuring what you send a model: giving context, stating the desired format, supplying examples, and decomposing a hard request into steps. It became briefly famous as a supposed new profession, complete with salary headlines.
That framing has aged badly, for a good reason: models improved. Many techniques that mattered a few generations ago are now unnecessary, because models handle underspecified requests far better. The elaborate incantations circulated in prompt-trading marketplaces were mostly working around limitations that no longer exist.
What remains is durable and unglamorous. Supplying relevant context still beats asking more forcefully. Being explicit about format still helps. Examples still work for anything with a specific shape. And for production systems, the actual discipline is evals — measuring whether a change helped, rather than concluding from one good output that it did.
Why it matters
Prompting is the highest-leverage skill for most people using AI, and it costs nothing to develop. But the industry oversold it as a standalone career, and treating it as one leads teams to hire for the wrong thing — the scarce skill is evaluating AI systems, not phrasing requests to them.
In practice
Context, format, examples, decomposition — in that order. Don't buy prompt packs. If a prompt matters enough to optimize, it matters enough to measure with an eval.