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The craft of writing and structuring prompts — instructions, examples, format — to reliably get the output you want from a model.
Prompt engineering is the systematic part of working with models: being specific about the task, giving few-shot examples, specifying output format, assigning a role, and stating constraints so the model doesn't guess. It matters because the same model can go from useless to excellent on identical inputs purely from how you frame the ask. The discipline is iterating against an eval rather than tweaking until one example looks good — otherwise you're just overfitting to vibes.
Plainly
Think of Prompt Engineering as a simple recipe for doing the work better. The craft of writing and structuring prompts — instructions, examples, format — to reliably get the output you want from a model.
In practice
Use it when you need a repeatable method instead of guessing from vibes. In practice, define the owner, input, output, and failure mode before you rely on it.