How to use Structured Schema Validator for Finance
Paste LLM JSON output and the page validates against four pre-built finance schemas — research output, trade decision, risk snapshot, peer comparison — with sanity checks beyond raw JSON-Schema (currency, ticker plausibility, magnitude bounds).
What It Does
Use the calculator with intent
Paste LLM JSON output and the page validates against four pre-built finance schemas — research output, trade decision, risk snapshot, peer comparison — with sanity checks beyond raw JSON-Schema (currency, ticker plausibility, magnitude bounds).
Engineers piping LLM JSON into a trading or research database who need a stricter check than raw JSON-Schema — catching plausibility failures, not just type mismatches.
Interpreting Results
Schema-validation failures are the structural bugs; sanity-check failures are the LLM bugs. A schema-valid response with magnitude-out-of-bounds is the model hallucinating a number that fits the type but not the reality.
Input Steps
Field by field
- 1
Pick option
Pick a reference schema (trade order, risk report, transaction extraction, KPI extraction, earnings summary) or upload your own.
- 2
Paste inputs
Paste a sample LLM output to validate.
- 3
Read outputs
Read pass/fail with per-field error details: missing required fields, type mismatches, enum violations, cross-field consistency failures.
- 4
For
For batch validation, upload a directory of outputs. Read aggregate failure rates and top-3 most common errors.
- 5
Use result
Use the failure-mode aggregate to debug your prompt or schema — repeated failures often point to ambiguous prompt language.
Common Scenarios
Use realistic starting points
Research output validation
Schema
research
Sample
earnings summary
Schema passes; sanity check flags one growth rate above 1000% — the model misread a basis-point number as a percent.
Trade decision validation
Schema
trade-decision
Sample
agent buy/sell call
Schema fails on missing risk-reason field; sanity check flags ticker ticker case mismatch (lower-case symbols).
Try These Tools
Run the numbers next
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.
Agent Skill Tester for Markets
Paste a SKILL.md definition + sample input + your Anthropic API key. See structured extraction, token cost, and latency — all in your browser. No signup.
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.
FAQ
Questions people ask next
The short answers readers usually want after the first pass.
Related Content
Keep the topic connected
Hallucination Detection
Detecting LLM hallucinations in financial outputs: the verifiable-claim approach, citation grounding, and cross-model agreement signals that work.
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.
HowTo Schema
Schema.org HowTo: the structured-data type for step-by-step procedural content. The fields that matter for agent ingestion vs the ones search ignores.