Prompt engineering

Core techniques are well established: state the task and constraints explicitly, show few-shot examples of desired output, delimit inputs clearly, ask for step-by-step reasoning on hard problems, and specify the output format precisely. As models improved, brittle incantations gave way to something closer to clear technical writing, but the leverage remains large — the same model can be mediocre or excellent depending on its prompt.

Prompting has a direct cost dimension because prompts are billed input. Few-shot examples, verbose instructions, and defensive boilerplate all scale linearly with request volume, so prompt optimization cuts both error rates and spend; a tighter prompt that maintains quality is a permanent discount.

Mature teams treat prompts as versioned, evaluated artifacts: changes run against an eval suite before shipping, and prompts are re-tuned when models are swapped, since phrasing that helps one model can be noise to another.