Hallucination Guardrails for Finance LLMs
A finance LLM that confidently states a wrong number is more dangerous than one that refuses to answer. This checklist layers the guardrails that catch fabrication before it reaches a decision.
Checklist Progress
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Checklist Sections
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Section 1
Phase 1: Grounding
Section 2
Phase 2: Numeric verification
Section 3
Phase 3: Faithfulness
Section 4
Phase 4: Gating and monitoring
Pro Tips
Small moves that make the checklist easier to finish
Try These Tools
Run the numbers next
Sources & References
- Survey of Hallucination in Natural Language Generation — Ji et al., ACM Computing Surveys (2023)
- OWASP Top 10 for Large Language Model Applications — OWASP Foundation (2023)
Related Content
Keep the topic connected
LLM for Finance Deployment Checklist
A pre-flight checklist for putting a large language model into a finance workflow: scoping, grounding, input security, numerical verification, and drift monitoring.
RAG for Filings Setup Checklist
RAG for filings checklist: chunk on structure, tune retrieval, enforce citations, verify numbers, and treat retrieved text as untrusted.
Hallucination Detection
Detecting LLM hallucinations in financial outputs: the verifiable-claim approach, citation grounding, and cross-model agreement signals that work.
Model Drift
Model drift: when an LLM's behavior changes between calls, versions, or weeks. The monitoring stack that catches it before production breaks.