Validate AI Output — Catch Format Violations
Paste the response, get the verdict: real JSON parsing, missing-field detection, and a repair prompt for everything found.
Overview
AI output looks right until something parses it. This setup validates the most common case — a JSON record that came back fence-wrapped, missing a required field, and carrying an invented one — and shows the full loop: a health score with a PASS/WARNING/FAIL verdict, every violation listed with its specific fix, and a repair prompt that sends the model back to correct its own response without touching what was already right. Validation is real: JSON.parse, not string matching.
Workflow
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Paste the response raw
Fences, preamble, and all — the wrapping is often the violation, and the validator detects it.
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Read the issues, not just the score
Each issue carries its fix; the score only tells you how bad the bleeding is.
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Send the repair prompt back
In the same conversation, so the model still has its previous response in context — it repairs, you re-validate.
Why This Works
- Real parsing finds what eyeballing misses — a trailing comma is invisible until JSON.parse
- Issue-specific fixes beat "please return valid JSON" regenerate-and-pray
- The repair loop preserves the 90% of the response that was already correct
Best for
- Anyone whose pipeline broke on output that "looked fine"
- Debugging AI steps in automations
- Teaching teams what format violations actually look like
Not for
- Defining the output contract up front — that's the JSON Output Prompt Builder
- Judging content quality — this validates structure, not substance
Use cases
- Checking a model response before code consumes it
- Seeing exactly which fields are missing or invented
- Getting a repair prompt instead of rewriting the request