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AI in Markets Checklist

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 ITEMSPublished May 26, 2026Live Content
By AI Fin Hub Research · AI Fin Hub Team

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Checklist Sections

Work in focused batches instead of one long wall

Section 1

Phase 1: Schema definition

3 items
Use The ToolPlaygrounds

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.

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Section 2

Phase 2: Parse and validate

3 items

Section 3

Phase 3: Finance sanity checks

3 items
Use The ToolPlaygrounds

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.

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Use The ToolPlaygrounds

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.

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Section 4

Phase 4: Failure handling

3 items
Use The ToolPlaygrounds

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.

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Pro 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 & References

Related Content

Keep the topic connected

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Prompt Design for Financial Extraction

Financial extraction prompt checklist: enforce a strict schema, require source grounding, handle missing values, and test adversarial filings.

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A pre-flight checklist for putting a large language model into a finance workflow: scoping, grounding, input security, numerical verification, and drift monitoring.

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Hallucination Detection

Detecting LLM hallucinations in financial outputs: the verifiable-claim approach, citation grounding, and cross-model agreement signals that work.

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FAQPage Schema

Schema.org FAQPage: the structured-data spec that makes FAQ content machine-readable for search and LLM crawlers. When to apply, when to skip.

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Planning estimates only — not financial, tax, or investment advice.