How do you know an LLM answer is actually grounded — not just plausible? I measured it across 7 models and 4 regulated domains
A developer has created a system to audit the accuracy of Large Language Model (LLM) answers, particularly in regulated domains where factual grounding is critical. The pipeline generates questions from source documents, has LLMs answer them with context, and then uses deterministic code to verify the answers against the source text. This auditing process significantly improved accuracy across seven tested models, with audited scores ranging from approximately 95% to 100% compared to baseline retrieval methods. AI
IMPACT This auditing method could significantly improve the reliability of LLM applications in critical sectors by ensuring factual accuracy.