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Why RAG fails in insurance — and what we built instead.

Retrieval-augmented generation works beautifully for general knowledge. It breaks in subtle, expensive ways when applied to insurance documents — cross-policy reasoning, dated endorsements, and conflicting clauses across a submission stack.

JO
Dr. James Okoro
Apr 14, 2025
journal text

Why RAG fails in insurance — and what we built instead.

Retrieval-augmented generation is one of the most useful architectural patterns to emerge from the LLM era. The idea is straightforward: rather than relying solely on a model's parametric knowledge, you retrieve relevant documents at inference time and condition the model's response on them. It works well for customer support, knowledge bases, internal wikis.

It does not work well for insurance — at least not without significant modification. We learned this the hard way. When we built the first version of Sovix's document intelligence layer in 2022, we started with a standard RAG pipeline. It failed in production within three weeks. Not catastrophically — the outputs looked plausible. That was the problem. Plausible but wrong is worse than obviously broken.

Why RAG fails in insurance — and what we built instead.

Retrieval-augmented generation is one of the most useful architectural patterns to emerge from the LLM era. The idea is straightforward: rather than relying solely on a model's parametric knowledge, you retrieve relevant documents at inference time and condition the model's response on them. It works well for customer support, knowledge bases, internal wikis.

It does not work well for insurance — at least not without significant modification. We learned this the hard way. When we built the first version of Sovix's document intelligence layer in 2022, we started with a standard RAG pipeline. It failed in production within three weeks. Not catastrophically — the outputs looked plausible. That was the problem. Plausible but wrong is worse than obviously broken.