Prompt Engineering

Repair AI Output with a Repair Prompt

Don't re-roll the whole response — send back a surgical prompt that fixes the violations and keeps everything that was right.

Open in AI Output Validator

Overview

When output breaks the rules, the reflex is to regenerate — and lose everything that was correct in the re-roll. The repair prompt is the better move: it restates the original requirement, lists each violation with its specific fix, and instructs the model to return only the corrected output, changing nothing else. This setup demonstrates it on a classification that invented a label ("Very Urgent") outside the defined set — one of the violations where a surgical fix beats a fresh attempt.

How to use this resource

Validate first

The repair prompt is generated from real findings — no findings, no repair, no busywork.
Send it in the same thread

The model repairs its own response best when that response is still in context.
Re-validate the repair

Paste the corrected output back in — PASS closes the loop; anything else gets a fresh repair prompt.

Why This Works

"Fix only the problems listed" constrains the model exactly where regeneration doesn't
Restating the original requirement re-anchors the contract
The validate → repair → re-validate loop converges in one or two rounds

Best for

Responses that are 90% right — most of them
Long outputs where regeneration costs real tokens
Conversations where the model's context is still warm

Not for

Content that is wrong rather than misformatted — bad facts need a better prompt, not a repair
Defining label sets up front — that's the Data Classification Prompt

Use cases

Fixing one wrong label without re-rolling the analysis
Correcting format violations in long responses cheaply
Standardizing the repair flow across a team

FAQ

How does a repair prompt fix one bad label without regenerating the whole response?

The OUTPUT RULES pin it down: "Fix only the problems listed above — do not change anything else" and "Keep everything that was already correct unchanged." In this example only the invalid "Very Urgent" label is flagged, so your assistant returns the corrected classification against the defined set and leaves the rest untouched — a surgical edit, not a re-roll.

Where does the repair prompt come from and where does the corrected output go?

The AI Output Validator generates the repair prompt from real findings, so no violations means no repair. You send it in the same thread where the model's response is still warm, then paste the corrected output back into the validator — a PASS closes the loop, anything else gets a fresh repair prompt. NewPrompt never runs or confirms the fix.

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 support ticket classification output against the routing label set.
Expected: exactly one of the labels, exactly as defined: Billing, Technical, Account, How-To Question, Feature Request.

PROBLEMS FOUND
- Invalid label: "Very Urgent" — not in the defined label set. Fix: choose exactly one of: Billing, Technical, Account, How-To Question, Feature Request.

OUTPUT RULES
- Return only the corrected classification — 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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Guides for this resource

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