Prompt diagnosis and rewrite workbench
Paste a rough AI prompt. The tool shows you which instruction layers are missing, then rebuilds the thing into a clearer brief you can actually test. You get a comparison of what was there versus what got added, a short pile of clarifying questions, and a copy button for the long version or the quick one. Honestly, most weak prompts fail for one boring reason, and it shows up fast here.
The rewrite gets built locally, right here, from your prompt and the settings you picked. Treat it as a better starting point. Then edit it for the actual facts and the real boundaries of your task, because the tool can’t know those.
Why weak prompts need diagnosis before a rewrite
Here’s the thing nobody tells you. A weak prompt usually isn’t weak because the wording is clumsy. It’s weak because the job is underspecified. Take “write about DNS.” That leaves audience, purpose, depth, the rules about facts, the output shape, and how you’ll even know it worked all wide open. The assistant fills those gaps with something. The answer reads smooth and still misses what you actually wanted.
So this improver starts by diagnosing, not rewriting. It hunts for signals: is there an audience? A deliverable? Context, constraints, examples, rules about evidence, some way to verify the thing. Then it rebuilds a brief around your original request rather than swapping in unrelated boilerplate. And that’s what makes the rewrite easier to trust. You can see which layer was already there, which one got added, plus the question that still needs a human to answer it before you hit send.
What a better prompt changes
Good improvement keeps your intent and just cuts the guesswork. It might pin down a format. Name the person who’ll read the output. Spell out which facts are fair game, ask for assumptions to be made visible, set a quality bar. For code review the weight shifts toward bugs and regressions and the tests that should exist but don’t. SEO content? You’d add reader intent, a couple of real examples, the honest limitations. Research leans on how evidence gets handled and where the uncertainty sits.
- Summary scores how much of the instruction is actually there, before you rewrite anything.
- Improved prompt hands you a detailed brief. Edit it, send it.
- What changed lines up the prompt ingredients side by side. It doesn’t pretend to show some magical diff, because there isn’t one.
- Questions collects the missing decisions, the ones worth settling before you do real work.
- Short version is a tighter prompt for when the chat around it already carries the context.
How to improve prompts without making them robotic
- Keep the original intent visible. Otherwise the rewrite drifts into a generic framework that answers nobody’s question.
- Add the audience and the deliverable first. Those two choices, honestly, shape most of what comes back.
- Add constraints where a failure would actually sting: facts, scope, tone, which files to touch, format.
- Lean toward output shapes you can review, so the first pass is easy to correct.
- Once the answer lands, fix the weakest part with a focused follow-up. Don’t restart from zero, that’s just slow.
Human editing still matters
Look, a rewrite tool can surface the missing pieces. What it can’t do is read the private facts sitting in your head. Say the prompt reads “fix the page” and you happen to know the real problem is mobile overflow that started after a theme change. That fact belongs in the prompt, full stop. And if a draft is heading for publication, a human still has to call it: are the examples truthful, does the tone match the site, does the thing help an actual person instead of just filling a template. Maybe it’s just me, but that last check is the one people skip most.
Common questions
Should every prompt include examples?
No. Examples earn their keep when format or tone or some weird edge case really matters. When the task is already crystal clear and you’ve spelled out what counts as done, they’re optional. Skip them.
Why show questions instead of filling everything automatically?
Because some of what’s missing isn’t a wording gap. It’s a real decision only you can make. Guess the audience or guess where the facts come from, and you get a polished prompt aimed at the wrong target. Polished and wrong is still wrong.
Does a better prompt guarantee a correct answer?
Nope. All it does is improve the brief. Factual claims still need checking. Code still needs a review and tests that actually run. And anything you publish still needs a human with editorial judgment to look it over first.
How do I turn a weak prompt into a strong one?
Start with the role and the goal. Hand over the context that’s missing. Say what the output should look like and roughly how long. Then list your constraints and the stuff you don’t want. One more thing that helps a lot: swap vague verbs for instructions that say exactly what to do.
Does a longer prompt always work better?
No, and people get this backwards all the time. Relevance beats length. Keep the detail that actually changes the answer, cut the rest. A tight prompt carrying the right context will beat a long rambling one most days.
Should I tell the model what not to do?
Yeah, negative constraints do help. Just pair each one with the positive version. Telling it what to do instead of only what to avoid tends to land more reliably, in my experience anyway.
