Prompt Engineering

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.

Open in AI Output Validator

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.

How to use this resource

Paste the response raw

Fences, preamble, and all — the wrapping is often the violation, and the validator detects it.
Read the issues, not just the score

Each issue carries its fix; the score only tells you how bad the bleeding is.
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

FAQ

How does the AI output validator catch a missing field in JSON?

It parses the response with JSON.parse rather than string matching, then checks it against the expected object — fields customer_name, email, plan, and active — and reports each violation with its fix, like "Missing required field: email" (use null if unknown, never invent one). The validator surfaces issues and a repair prompt; it can't guarantee catching every structural problem in unusual output.

What do I do with the repair prompt this generates?

Send it back in the same conversation, so the model still has its previous response in context — it then fixes only the listed problems (remove the ``` fences, add the missing email, drop the unexpected customer_phone_number) and keeps everything already correct unchanged. You run the repair prompt in your own assistant and re-validate the result; nothing is auto-corrected here.

Does this check whether the AI's answer is factually correct?

It validates structure, not substance — notFor is explicit that judging content quality is out of scope. It confirms the record is parseable JSON with exactly the defined fields and no invented ones like customer_phone_number, and reports a PASS, WARNING, or FAIL health verdict. Whether the values are true or the answer is any good stays your review.

Customize This Resource

Opens this setup in AI Output Validator. Validate to see the score, every issue found, and the repair prompt to send back to the model.

Open in AI Output Validator

Prompt Template

Copy it as-is, or use Open in AI Output Validator to load it pre-filled and customize it with your own context.

REPAIR TASK
Your previous response did not follow the required format. Fix the problems listed below and return only the corrected output.

ORIGINAL REQUIREMENT
Validate the JSON customer record returned by the support assistant.
Expected: a single JSON object with the fields: customer_name, email, plan, active.

PROBLEMS FOUND
- The JSON object is wrapped in markdown code fences. Fix: remove the ``` fences — return the raw JSON only.
- Missing required field: email. Fix: include "email"; use null if the value is unknown — never invent one.
- Unexpected field: customer_phone_number. Fix: remove "customer_phone_number" — only the defined fields belong in the output.

OUTPUT RULES
- Return only the corrected JSON object — no explanations, no commentary, no code fences.
- Keep everything that was already correct unchanged.
- Fix only the problems listed above — do not change anything else.

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AI Output Validator

Paste an AI's output and validate it against the expected format — with a repair prompt for every failure found.

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