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LLM Output Schema Validation Checklist

Structured output from an LLM looks reliable until the day a field is missing, a number is out of range, or the JSON is malformed and a downstream system acts on garbage. This checklist covers validating output before it is consumed.

12 items. Published May 26, 2026. By AI Fin Hub Research.

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Sections

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Section 1

Phase 1: Schema definition

3 items

Use the Structured Schema Validator for Finance: Paste LLM JSON output and validate against four pre-built finance schemas — research output, trade decision, risk snapshot, peer comparison — with sanity.

Section 2

Phase 2: Parse and validate

3 items

Section 3

Phase 3: Finance sanity checks

3 items

Use the LLM Finance Error Taxonomy: 12 documented LLM-on-finance failure modes (hallucinated ticker, stale price, units, currency, off-by-100, fictional source, more). Paste output, see flags.

Use the Hallucination Detector: Paste a source document + an LLM's extraction. Every numeric claim in the output is checked against the source. Client-side. Catches silent fabrication.

Section 4

Phase 4: Failure handling

3 items

Use the Prompt Regression Tester: Run the same prompt against multiple models (Claude 4.5/4.6/4.7, GPT-5, Gemini 2.5) with your own keys. Diff outputs, score drift, catch regressions.

Tips

Small moves that make the checklist easier to finish

Schema-valid is not the same as correct. A negative share price and a revenue figure off by a factor of a thousand both parse perfectly, which is why range and sanity checks sit on top of type validation.
Fail safe, never best-effort. The moment a validator passes through a nonconforming output because rejecting it was inconvenient, the whole guardrail becomes decoration.
Feed the validation error back into a retry before escalating. Models correct their own formatting mistakes reliably when told exactly what was wrong, which saves the human gate for genuine problems.

Sources

  1. OWASP Top 10 for Large Language Model Applications , OWASP Foundation (2023)
  2. Artificial intelligence in UK financial services 2024 , Bank of England and Financial Conduct Authority (2024)

Keep the topic connected

  • Checklist Prompt Design for Financial Extraction

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  • Checklist LLM for Finance Deployment Checklist

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  • Learn LLM Hallucination Detection in Finance

    How to detect LLM hallucinations in financial outputs: citation grounding, verifiable-claim checks, and cross-model agreement that flag fabricated data.

  • Learn FAQPage Schema: JSON-LD Spec & When to Use

    FAQPage schema explained: the Schema.org JSON-LD type that makes FAQ content machine-readable for search and LLM crawlers, plus when to apply or skip it.