Usage & Prompting
Prompt Engineering
The craft of framing tasks so that a model reliably delivers something usable: defining a role, specifying a format, showing examples, naming edge cases. Part of it is craftsmanship, and that stays; the magic-formula part goes stale with every model generation. Anyone investing today is better off investing in a good task description than in clever phrasing tricks.
An example
One team tinkers with the wording of an instruction for two weeks and measures nothing. In the end the prompt is three times as long, everyone is satisfied, and nobody can say whether it has gotten any better. The other team first assembles thirty real cases with the expected output, measures the hit rate of the simplest possible prompt, and then changes one thing at a time. After two days the rate has risen from 61 to 88 percent, and they know which change did what.
A closer look
What reliably helps is short: state the task clearly, specify the desired format, show one or two examples, supply the context. Everything else depends on the model and its version and has to be measured rather than believed.
Why it matters
Prompt engineering without measurement is a debate about taste dressed in technical vocabulary. The whole value of the discipline lies in the feedback loop: a test set of real cases, a measure of what counts as correct, one change at a time. That turns tinkering into a procedure others can repeat. As a side benefit it solves the staffing problem: a measured prompt can be handed over, a gut-feel one cannot. And it makes model switches manageable, because the same test set immediately shows what the new model does worse.
Commonly misunderstood
Formulas found for one model age with the next version. Without test cases, nobody notices when a proven instruction performs worse after an update.