Hallucination Control
Techniques and safeguards that reduce how often large language models produce plausible-sounding but factually incorrect outputs in insurance use.
technicalPublished 2026/06/07Last verified 2026/06/07
FAQs
- Can we ever use an LLM for coverage determinations without human review?
- In practice, most carriers treat LLM outputs as decision support rather than autonomous decisions for any coverage determination that has legal or financial consequences. Full automation without human review is generally inadvisable until the model's error rate on your specific policy forms and claim types has been validated extensively in a controlled setting.
- How do we measure hallucination rates for an LLM deployed in our claims workflow?
- The standard approach is to create a benchmark dataset of questions with known correct answers drawn from your policy forms and claims records, run the model against this set, and have subject matter experts score the outputs for factual accuracy. Periodic re-evaluation tracks whether updates to the model or retrieval index change the hallucination rate.
- Does retrieval-augmented generation eliminate hallucinations entirely?
- No. RAG substantially reduces hallucination by grounding responses in source documents, but models can still misinterpret retrieved passages, fail to retrieve the relevant document, or generate inaccurate summaries of correct source text. RAG shifts the error mode toward retrieval failure, which is often more detectable and manageable than parametric hallucination.
Related Terms
Retrieval-Augmented Generation
An AI architecture grounding an LLM's responses by retrieving relevant documents or policy text from a knowledge base before generating an answer.
Model Governance
Policies, controls, and oversight processes managing the full lifecycle of predictive and AI models from development through retirement.
NLP Submissions
Applying natural language processing to extract structured risk data from unstructured insurance submissions, emails, and supplemental documents.
AI Model Audit
A structured review of an AI or statistical model's design, training data, outputs, and deployment to verify accuracy, fairness, and regulatory compliance.