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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.

technicalPublished 2026/06/07Last verified 2026/06/07

FAQs

How do we keep the RAG knowledge base current as policy forms and endorsements change?
Standard practice is to re-index documents whenever they are updated in the source system of record. Automated pipelines that monitor the policy form repository and trigger re-embedding on change events keep the retrieval index synchronized. Version control on the index allows you to trace which document version was active at the time of any given query.
What is the difference between RAG and fine-tuning an LLM on insurance data?
Fine-tuning bakes knowledge into model weights during training, which makes updates expensive and creates a static snapshot. RAG keeps knowledge external and updateable without retraining. For insurance applications where policy forms, regulations, and guidelines change frequently, RAG is generally preferred for factual recall tasks. Fine-tuning is better suited to adapting model style or reasoning patterns to insurance-specific formats.
Does RAG require a proprietary LLM or can it work with commercially available models?
RAG is model-agnostic and works with any LLM that accepts a context window. Carriers can combine proprietary retrieval pipelines over their own document repositories with commercially available foundation models, maintaining control over the knowledge base while leveraging the language generation capabilities of leading models.

Related Terms

  • Vector Embeddings

    Numerical representations of text or data in high-dimensional space, enabling semantic similarity search across insurance documents and claims.

  • Hallucination Control

    Techniques and safeguards that reduce how often large language models produce plausible-sounding but factually incorrect outputs in insurance use.

  • NLP Submissions

    Applying natural language processing to extract structured risk data from unstructured insurance submissions, emails, and supplemental documents.

  • Insurance Data Lake

    A centralized repository storing large volumes of raw structured and unstructured insurance data in native format for analytics, modeling, and reporting.

Related Items

  • Sixfold

    Generative AI underwriting agent for P&C and life

  • Convr

    AI submission intake and risk insight for commercial UW

  • Indico Data

    Intelligent intake for unstructured submissions

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Retrieval-augmented generation (RAG) is an AI architecture that improves the accuracy and verifiability of large language model outputs by retrieving relevant documents, passages, or records from an external knowledge base at query time, and supplying that retrieved context to the model as part of the prompt before generating a response.

How it works / Why it matters

A pure LLM generates responses entirely from patterns learned during training. It has no access to documents created after its training cutoff, cannot retrieve specific policy forms or claim records, and may fabricate plausible-sounding but incorrect information — the hallucination-control problem. RAG addresses this by adding a retrieval step:

  1. Query encoding: The user's question or input text is converted to a vector-embeddings representation.
  2. Semantic search: The encoded query is matched against a vector index of the knowledge base — which may contain policy forms, endorsements, claims guidelines, underwriting manuals, or regulatory bulletins — to retrieve the most semantically relevant passages.
  3. Augmented generation: The retrieved passages are injected into the LLM prompt as context. The model is instructed to base its answer on the provided context and to indicate when the answer cannot be found in the retrieved documents.
  4. Source attribution: The generated response includes citations pointing to the specific document sections used, enabling a human reviewer to verify accuracy.

In insurance, the knowledge bases powering RAG applications include policy form libraries, coverage interpretation guidelines, state-specific endorsement catalogs, claims handling manuals, and regulatory bulletins. Because the retrieval step can be updated continuously as documents change, RAG systems remain current without requiring model retraining.

In practice

A personal lines carrier might deploy a RAG system that allows claims handlers to query the policy form applicable to a specific claim in natural language: "Does this homeowners policy cover foundation damage caused by soil settlement?" The system retrieves the relevant exclusion and coverage provisions from the specific form edition on the claim, and generates a response citing those provisions — which the adjuster reviews before acting.

For commercial submission triage, a RAG pipeline can answer underwriter questions about a submission: "What prior losses does this account show and how do they compare to our appetite guidelines?" by retrieving from the submission documents and the carrier's appetite documentation simultaneously.

Platforms such as Sixfold apply RAG architectures to submission underwriting research. Convr uses similar retrieval patterns for submission data extraction and enrichment.

Related concepts

See vector-embeddings for the representation technique that powers the retrieval step, and nlp-submissions for a primary use case where RAG is applied in commercial underwriting workflows.