Iterate and Test your Prompts

Prompts are almost never right on the first draft. Iterative prompting is the loop of writing a prompt, running it against real inputs, studying where it fails, and revising — the same edit-run-debug cycle you already use for code, applied to natural language. The craft isn’t producing a perfect prompt from intuition; it’s converging on one through evidence.

This matters because LLMs are non-deterministic and their failure modes are unintuitive: a prompt that aces your three test cases can fall apart on the fourth phrasing of the same question. Shipping an untested prompt is shipping untested code. Worse, prompts silently regress — a wording tweak that fixes one case can break two others, and a model version upgrade can shift behavior under a prompt you never touched. Without a testing habit, you find out from users.

In practice, you’ll treat prompts as tested artifacts. Build a small eval set — 20 to 50 representative inputs with expected outputs, including the ugly edge cases — and rerun it after every prompt change, exactly like a test suite. Change one variable at a time so you know what actually helped. Keep prompts in version control with your code, and once the workflow matters, wire the evals into CI using tools like promptfoo or your provider’s eval APIs. Iteration turns prompting from guesswork into engineering.