Resources · 81
AI extraction: validate JSON before integration
Separate parsing, schema checks, documentary evidence and business rules when turning documents into usable records.
· 4 min
What this guide helps achieve
- Define the result contract
- Connect values to evidence
- Validate in an explicit sequence
- Test difficult documents
- Plan recovery and correction
Quick check
- Can valid JSON contain an error?
- Should a missing field become zero?
- When is human review needed?
Step-by-step method
- 01
Define the result contract
List necessary fields, types, units and missing-value states. JSON Schema distinguishes described properties from required fields: declaring a property does not make it mandatory. Decide whether extra fields are allowed. Retain the schema version for each batch and avoid collecting fields without a purpose.
Deliverable: versioned schema and valid or rejected examples.
- 02
Connect values to evidence
Require page, passage or cell references for values that trigger an action. Allow an unknown state when information is absent. A correctly typed amount can belong to another document; a plausible date can be the issue date instead of the due date.
Deliverable: field-to-evidence mapping and interpretation rule.
- 03
Validate in an explicit sequence
Check JSON parsing, schema conformity and business rules such as allowed currency, existing identifier, line totals and chronology. Schema conformity proves neither factual accuracy nor permission to write into a system. Keep business actions blocked until their conditions are met.
Deliverable: distinct error classes and acceptance decisions.
- 04
Test difficult documents
Include split tables, multiple languages, conflicting values, empty documents and hostile instructions inside source text. Test refusals, truncation and retries. Keep evaluation cases separate from tuning and compare errors by field instead of treating an overall rate as a guarantee.
Deliverable: reference set and critical error list.
- 05
Plan recovery and correction
Link document, model version, schema and result to a processing identifier. A retry must not create two records. Retain the reason for human corrections and locate downstream outputs. Avoid unnecessarily copying complete documents into logs.
Deliverable: correction and controlled retry procedure.
From readable output to an authorized action
Passing one check does not replace the next. Stop when a necessary condition is not met.
Parse JSON
Is the response complete and parseable?
Validate the contract
Do types, fields, units and missing states match the schema?
Verify values
Do source evidence and business rules support the values?
Authorize the action
Are identity, scope and writing conditions satisfied?
Fictional example: parsing and schema checks pass, but two amounts conflict. Writing stays blocked and the record goes to review.
Reusable worksheet
Complete with your authorised observations. These fields are a working template, not observed results.
| Field | Information to record |
|---|---|
| Contract | Fields, types, units, missing states and schema version |
| Evidence | Document and passage supporting each decisive field |
| Validation | Parsing, schema, business checks and rejection reason |
| Recovery | Processing identifier, duplicate prevention and propagated correction |
Worked example
Illustrative situation
Fictional example: an extractor finds two invoice totals and selects the pre-tax amount while the connector expects the amount due.
Decision and expected evidence
The schema accepts the number, but reconciliation of lines and currency blocks the write and sends the document for review.
Distinguish the mechanisms
| Mechanism | Purpose | Check or limitation |
|---|---|---|
| Parseable JSON | Allow parsing | Does not validate required fields or meaning |
| Schema conformity | Check types and constraints | Does not establish source fidelity |
| Business reconciliation | Check fitness and consistency | Needs explicit rules and references |
Management indicators
| Indicator | What it measures | First action |
|---|---|---|
| Field accuracy | Correctly extracted values among evaluable fields | Prioritize errors changing an action |
| Useful abstention | Cases left undecided because evidence is missing | Review the queue without encouraging invented values |
| Retry duplicates | Repeated records for the same processing operation | Correct deduplication |
Common pitfalls
- Does not validate required fields or meaning
- Does not establish source fidelity
- Needs explicit rules and references
Frequently asked questions
Can valid JSON contain an error?
Yes. Syntax and schema may be valid while an amount, identity or date is wrong. Check decisive values against their sources.
Should a missing field become zero?
Only when the contract assigns that meaning to zero. Otherwise use the agreed missing state; invented defaults distort calculations.
When is human review needed?
When conflicts, missing evidence or critical errors fall outside the accepted scope. Define these cases before processing batches.
Further reading and tools
Editorial resources to extend the method.
- WORLD AI GUIDE — choose an AI tool External link
Compare tool families before testing your extraction workflow.
- IIS — verify an AI answer External link
Complement format validation with a review of claims and sources.
Official references
References consulted: . The method and worksheet propose checks to adapt to your context; they do not constitute certification.
Continue exploring
Related analysis
