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Checklist, in AI in Markets

Prompt Design for Financial Extraction

Extracting structured data from filings, transcripts, and reports is one of the highest-value finance LLM tasks and one of the easiest to get subtly wrong. This checklist governs the prompt that turns a document into structured fields.

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

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Sections

Work in focused batches instead of one long wall

Section 1

Phase 1: Output contract

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.

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.

Section 2

Phase 2: Grounding

3 items

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 3

Phase 3: Missing and ambiguous data

3 items

Section 4

Phase 4: Testing and stability

3 items

Use the Prompt Injection Tester: Red-team a finance agent against 23 documented prompt-injection attacks — direct override, role confusion, indirect injection via retrieved content.

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

Units and currency are not pedantry, they are where finance extraction breaks. A revenue figure pulled as a bare number is one thousands-separator away from being wrong by a factor of a thousand.
Give the model an explicit way to say not-present. Without one, a model under pressure to fill a field will invent a plausible value, and a plausible wrong number is the hardest kind to catch.
Require a source span for every value. It turns verification from a judgment call into a lookup, and it makes the whole extraction auditable by someone who never saw the prompt.

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

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  • Learn LLM Hallucination Detection in Finance

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