Separate symptom from expectation
The prompt keeps what is actually happening and what should happen instead as two distinct sections, so the model isn't left guessing which one is the bug.
STRUCTURED DEBUG PROMPT
Debug Prompt Builder
Turn a symptom, expected behaviour, context, and evidence into a structured debugging prompt an AI coding tool can actually act on.
- Uses your inputs in a transparent calculation
- Instant result with practical next steps
- No signup required
Planning guidance only. Validate important decisions with customer evidence and your delivery team.
YOUR INPUTS
Build your result
Complete every field. The result updates only when you choose Calculate.
How it works
Attach context and raw evidence
Environment, recent changes, and the exact error text or stack trace are quoted verbatim in a fenced block rather than paraphrased, which keeps the model from missing details you noticed but didn't restate.
Score completeness before you send it
Each section is checked for specificity — word count, presence of error text, file or line references, and what you've already tried — and rolled into a single completeness score.
Continue learning: How AI coding tools invent fake packages · How to evaluate AI coding tools for your team
Frequently asked questions
Why does the tool ask what I've already tried?
Without it, an AI coding tool commonly re-suggests a fix you already ruled out, wasting a round trip. Listing dead ends up front keeps the model's hypotheses moving forward.
What if I don't have an exact error message yet?
Leave the evidence field with whatever you do have — console output, a screenshot description, or the closest log line. The score will flag missing error text as a gap, and the prompt will explicitly ask the model for a way to surface one.
Does a high score guarantee the AI tool will find the bug?
No. The score measures how complete and specific your prompt is, not the difficulty of the underlying bug. A well-specified prompt just gives the model a fair shot instead of asking it to guess at missing information.
Should I paste my whole file as context?
Only the relevant part. Name the file and the surrounding function so the model knows where to look; paste the full file only if the tool you're using has already loaded your repository and just needs orientation.
Can I reuse this prompt for a follow-up if the first fix doesn't work?
Yes — update the evidence field with the new error or behaviour and regenerate rather than starting over. Keeping the original symptom and expected behaviour keeps the model from re-litigating settled context.
How We Compare
| Feature | MVPHub | GitHub Copilot | Cursor |
|---|---|---|---|
| Focused input-based assessment | Included | Limited | Limited |
| Transparent calculation | Included | Limited | Limited |
| No repository access required | Included | Limited | Limited |
| Workflow-specific next steps | Included | Limited | Limited |
GitHub Copilot and Cursor can inspect open files and terminal output directly inside the editor. MVPHub instead structures the symptom, evidence, and context you provide into one complete prompt you can paste into either tool, or any other AI assistant.
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