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Self-Refine Iterative Output Improvement
Language AI
Language AI GUIDE
Structured Outputs
Structured outputs organize model responses into a defined shape, such as a JSON object validated against a schema.
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Overview
This makes integration easier, but structural validity does not establish factual correctness. A valid object can still contain an invented value or an inappropriate decision.
Key takeaways
- Define missing-value and versioning rules.
- Handle incomplete or refused outputs.
- Check factual relationships after schema validation.
Deep Dive
Specify the fields, types, allowed values, and missing-value behavior before generating output. A date string, a numerical amount, and a list of source identifiers need different validation. Define whether additional fields are accepted and whether an absent value differs from an explicit null.
Providers vary in how they enforce structure. Some constrain generation against a supported schema; others rely on prompting and validation afterward. Check the supported schema features and still handle refusals, truncation, timeouts, and malformed responses at the application boundary.
Validate meaning after shape. An extracted total should agree with the document and any applicable arithmetic. A cited source identifier must refer to a source actually supplied. A schema cannot ordinarily decide these relationships by type checking alone.
Keep the schema version with stored outputs and define compatibility rules before changing it. Downstream systems should reject or explicitly adapt incompatible versions. Avoid silently substituting fabricated defaults when a required piece of evidence is absent.
04Worked example
Catch a valid but incorrect object
Use an invented receipt with subtotal 50, tax 5, and total 55. A model returns a valid object containing total: 550.
The value passes a numerical type check but fails comparison with the source and the subtotal-plus-tax relationship.
Flag it for correction and preserve the supporting text instead of accepting schema validity as proof.
What it shows
The exercise distinguishes structural validation from evidence-based validation.
Real-World Implementation
Extract invoice fields with explicit missing values and source references.
Validate a classification output against a finite list of supported labels.
Putting it into practice
Why it matters
- Speed and scale. Language workflows can move faster without sacrificing consistency.
- Access and reach. It expands access across languages and communication styles.
- Clearer decisions. Teams can spend more time on judgment while automation handles repetition.
Watch out for
- Hallucinated facts can quietly enter reports, support flows, or research outputs.
- Prompt sensitivity can create inconsistent results across similar requests.
- Sensitive text data may be exposed if access controls are weak.
Next steps
- Define output format, tone, and quality standards before rollout.
- Ground responses with trusted sources whenever accuracy matters.
- Keep a human review checkpoint for high-stakes outputs.
- Track failure patterns and retrain prompts or workflows regularly.
Sources and further reading
- JSON SchemaJSON Schema object reference
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Frequently asked questions
Does guaranteed JSON mean a guaranteed correct answer?
No. JSON validity concerns syntax and structure. The values still need verification against the task and evidence.
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