Prompt Acceptance Criteria: the fastest way to get reliable AI outputs

If you’ve ever thought “the model ignored what I said”, there’s a good chance you didn’t actually say it in a way that can be checked. Most prompts describe intent (what you want) but skip acceptance criteria (how to tell it’s done). In software, we don’t ship “make it nicer” — we ship “passes these tests”. Prompting works the same way. This post is a practical pattern I use constantly: Write the acceptance criteria first. Then write the prompt. It sounds boring. It’s also the fastest way to turn flaky outputs into repeatable results. What “acceptance criteria” means for prompts Acceptance criteria are verifiable requirements for the output. They answer: What must be included? What must be excluded? What format should the answer have? What quality bar should it meet? How will I quickly review it? When you include these, you’re not “overprompting”. You’re giving the model a target it can aim at. A simple mental model: Intent : “Summarize this doc.” Criteria : “7 bullets, each starts wit