Resource
Fix Invalid JSON from AI
The JSON won't parse and you can't see why. Deterministic cause-sniffing — trailing commas, single quotes, unclosed brackets — and the repair prompt that fixes it.
Validate Structured Output from AI
Fields checked against the contract: missing ones flagged, invented ones caught, prose around the object detected.
Overview
Structured output drifts in three ways: fields go missing, fields get invented, and prose creeps in around the object. This setup validates an extraction response that does all three at a typical scale — a friendly "Sure! Here is the extracted data:" preamble, a missing timeline field, and an invented notes field. The validator separates them by severity (missing = FAIL, invented = WARN, prose = WARN), and the repair prompt addresses each one specifically, including the null-over-invention rule for the missing field.
How to use this resource
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Reuse your generator's field list
The fields you defined in the extraction prompt are exactly the expected structure here.
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Mind the severity split
Missing fields fail; invented fields warn — your pipeline may tolerate extras but never absences.
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Repair with null discipline
The repair prompt says "use null if the value is unknown — never invent one"; the fix doesn't become a new violation.
Why This Works
- Field-set comparison catches both directions of drift
- Severity weighting mirrors real consumer tolerance
- The null-over-invention repair rule closes the loop the contract opened
Best for
- Extraction pipelines with downstream consumers
- Outputs that must match a field list exactly
- Debugging "where did this field come from?" incidents
Not for
- Defining the extraction fields — that's the Extraction Prompt Generator
- Checking whether extracted values are TRUE — structure validation, not fact-checking
Use cases
- Auditing extraction output before it enters the CRM
- Catching invented fields that schema-less checks miss
- Stripping conversational wrapping from data responses
FAQ
Why is a missing field treated more seriously than an extra field in this validator?
The severity split weights them differently: the missing "timeline" field is a FAIL while the invented "notes" field is only a WARN. A consumer reading the six expected fields can usually ignore an extra key, but a downstream absence breaks it. The REPAIR TASK flags both directions and gives a specific fix for each; you decide which one blocks your pipeline.
Does the repair prompt fix a missing field by making up a value for it?
It fixes the absence without inventing content. The rule for the missing "timeline" reads "include timeline; use null if the value is unknown — never invent one," so the repair adds the key with null rather than a guessed date. That keeps the correction from becoming a second violation. You paste the repaired output back into your own assistant.
What does the ai output validator hand back when it runs this?
A REPAIR TASK prompt listing the problems it found — prose outside the JSON, the missing "timeline", the unexpected "notes" — each with a specific fix. The validator surfaces and packages the issues; it does not re-run your model or promise it caught every drift. You paste that repair prompt back to the assistant that produced the bad output.
More resources from AI Output Validator
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Resources that pair well
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Force JSON Output from AI
Stop getting 'Sure, here is the JSON…' — the output-contract pattern that forces models to return only parseable JSON: schema, example, and a strict rule block.
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Free text in, named fields out. The extraction prompt pattern that turns any unstructured text into consistent, parseable records.
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Classify Support Tickets with AI
Billing, Technical, Account, How-To, Feature Request — ticket triage with definitions that decide the borderline cases for the model.
Related tools
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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.
Projects that use this resource
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Build a Data Pipeline with AI
The full path to a pipeline that moves data without corrupting it — design the ingestion and transforms, extract and structure the sources, gate the quality, store it, then deliver and ship it monitored.
Workflows that use this resource
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AI Data Extraction Workflow
Turn messy text into structured data you can trust enough to feed another system — bound the source, extract the fields, force clean JSON, and validate before it flows downstream.
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AI Programmatic SEO Workflow
Generate many intent-matched SEO pages from a template without the thin-content spam — map the search intents, define the page variables, build the template, and QA before publishing at scale.
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AI Data Pipeline & ETL Workflow
Design a pipeline that moves data without corrupting it — map the sources and ingestion, design the transformation stages, set validation and quality gates, then document the pipeline and monitoring.
Guides for this resource
Guide
How to Extract Data From Documents With AI Without Losing Evidence
AI pulls the fields you asked for, but hands back a flat list with no way to tell which values it read from the document and which it guessed. Here's how to make each extracted value carry its source quote, location, and a review flag — so you can check the result instead of trusting it.
Guide
Define Fields So AI Fills Them the Same Way Every Time
You ask AI to fill a table — issue_type, priority, reason — and the same column gets filled by different logic each row: priority as urgency here, sentiment there. Here's how to define each field's meaning, allowed values, and include/exclude rules so AI fills them consistently.