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 optimisation 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.