# AI Foundations 6 - Prompting and Evaluation > Source: > Published: 2026-09-12 12:00:00+00:00 # AI Foundations 6 - Prompting and Evaluation 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. Previous: [AI Foundations 5 - Tool Calling](/2026/09/13/tool-calling-basics/) Next: [AI Foundations 7 - Agents and Autonomous Loops](/2026/09/13/agents-autonomous-loops/)