The exact same model can look brilliant or mediocre depending entirely on how you ask it something. Swap a vague instruction for a specific one, and the quality, the format, even the correctness of the answer can shift noticeably — without touching the model at all.
Prompt structure #
A prompt that works tends to spell out the same handful of things: what the task actually is, what context matters, and what shape the answer should take. Keeping the role, the task, any constraints, and any examples visibly separate makes a long prompt easier for the model to parse — and easier for you to debug later.
Few-shot examples #
Rather than describing the format you want, show it. A couple of example input/output pairs, close in style to the real task, often steers a model faster than another paragraph of instructions would.
Common techniques #
A few moves come up again and again: split a big task into smaller ones instead of asking for everything at once, ask the model to reason through the problem before it commits to an answer, and treat a first attempt at a prompt as a draft — refine it once you see where it actually goes wrong.
Why evaluation matters #
Change a prompt to fix one failing case, and you can quietly break three others you weren’t even looking at. Checking a handful of outputs by eye won’t catch that. You need something that runs every time you change something, not just when you remember to look.
Building an eval #
Start with a set of real test cases — genuine edge cases and past failures work better than made-up ones. Decide up front what counts as passing. Then run that same set automatically whenever the prompt, the model, or the setup changes, instead of testing by hand each time.
Grading approaches #
Not every output can be checked the same way. A structured answer can often be checked with an exact match or a simple rule. An open-ended answer — a summary, an explanation — usually needs another model to judge it, or a person, when the stakes are high enough that a judge model’s opinion isn’t good enough on its own.
Iteration loop #
Change something, rerun the eval, compare the numbers to before. Repeat. Prompting isn’t a task you finish once — it’s closer to tuning, something you keep coming back to as the model, the task, or the data shifts underneath you.
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