Structured AI output needs validation at several levels: syntax, schema, business meaning and action authority. A response that parses as JSON can still contain an incorrect amount, unsupported claim or impermissible instruction. Keep validation failures visible and prevent uncertain outputs from silently becoming trusted operational records.
Let’s plan your AI output contract.
Bring your workflow, constraints and open questions. We’ll help you define the requirements and work through a practical next step.
Define the output contract
Specify required fields, types, allowed values and relationships. JSON Schema's object reference explains property and required-field behaviour; merely listing a property does not make it mandatory.
Record the schema version used for each accepted output. Keep provider-specific structured-generation features separate from the application's own validation and recovery responsibilities.
Build a validation ladder
| Layer | Question | Example failure |
|---|---|---|
| Parsing | Is the representation readable? | Truncated or malformed output |
| Schema | Are structure and required fields valid? | Missing identifier or wrong type |
| Domain rules | Are values permitted together? | End date before start date |
| Source support | Does evidence justify extracted facts? | Amount not present in the document |
| Authority | May this result trigger the proposed action? | Generated approval exceeds permissions |
| Persistence | Is the accepted record written safely? | Retry creates a duplicate |
Choose a response for each failure type. Repeating the same model request is not always the right remedy for invalid source data or missing authority.
Preserve uncertainty instead of inventing values
For a hypothetical invoice extraction workflow, a missing currency should not become an arbitrary default merely to satisfy a required string field. Define whether the output represents unknown explicitly or enters review.
Keep validation diagnostics useful but controlled. Logging entire sensitive documents to explain a missing field can create unnecessary exposure.
Test realistic contract failures
Include extra fields, unsupported enum values, inconsistent totals and valid-looking but unsupported references. Confirm downstream handlers reject or review them under the defined rules.
Evaluate semantic correctness separately from schema success rate. A model producing perfectly shaped wrong records can perform well on a structural metric while failing the business task.
Structured-output questions
Does schema-constrained generation remove the need for validation?
No. Verify the actual provider guarantees and retain application checks for business meaning and authority.
Should invalid outputs be repaired automatically?
Only under explicit rules with evidence and evaluation. A repair that guesses missing facts can make the output less trustworthy.
Use field-level document review, and discuss output contracts with Drema's AI development team.
This article explains what to build, not legal advice. Rules change; confirm against the current official text before relying on it.