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🤖 '''Retrieval-augmented generation (RAG)''' is an [[Definition:Artificial intelligence (AI) | artificial intelligence]] architecture that enhances [[Definition:Large language model (LLM) | large language model]] outputs by grounding them in real-time retrieval of relevant documents, making it especially valuable in insurance where accuracy and traceability are non-negotiable. Rather than relying solely on a model's pre-trained knowledge — which can hallucinate facts or drift from policy-specific language — RAG systems first search a curated knowledge base (such as [[Definition:Policy form | policy forms]], [[Definition:Underwriting guideline | underwriting guidelines]], or [[Definition:Claims management | claims]] documentation) and then feed the retrieved passages into the generation step. This means the AI's answer is anchored to actual source material, a critical requirement when outputs inform [[Definition:Coverage determination | coverage determinations]], [[Definition:Regulatory compliance | regulatory responses]], or [[Definition:Policyholder | policyholder]] communications.

⚙️ In practice, a RAG pipeline in an insurance organization starts with an indexing phase: internal documents — ranging from [[Definition:Reinsurance treaty | reinsurance treaties]] and [[Definition:Endorsement | endorsements]] to regulatory bulletins and [[Definition:Loss run | loss runs]] — are chunked, embedded as vectors, and stored in a searchable database. When a user poses a question (for example, "What flood sublimit applies to this commercial property program?"), the system retrieves the most semantically relevant document fragments and passes them as context to the language model, which then synthesizes a precise, citation-backed answer. [[Definition:Insurtech | Insurtech]] companies and large [[Definition:Insurance carrier | carriers]] deploy RAG within [[Definition:Chatbot | chatbot]] interfaces for agents, internal knowledge assistants for [[Definition:Underwriter | underwriters]], and automated drafting tools that pull exact clause language from approved [[Definition:Manuscript policy | manuscript policies]].

💡 The significance of RAG for the insurance industry lies in its ability to close the gap between powerful generative AI and the strict accuracy demands of a heavily regulated, document-dense business. Without retrieval grounding, a language model might fabricate policy terms or misstate [[Definition:Exclusion | exclusion]] language — errors that carry real financial and legal exposure. RAG mitigates this risk while still delivering the fluency and speed that make generative AI attractive, enabling faster [[Definition:Quote | quote]] turnaround, more consistent [[Definition:Claims adjudication | claims adjudication]] support, and scalable compliance review. As carriers invest in [[Definition:Digital transformation | digital transformation]], RAG has emerged as one of the most practical paths to deploying AI responsibly in high-stakes insurance workflows.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Large language model (LLM)]]
* [[Definition:Natural language processing (NLP)]]
* [[Definition:Insurtech]]
* [[Definition:Intelligent document processing (IDP)]]
* [[Definition:Digital transformation]]
{{Div col end}}

Definition:Retrieval-augmented generation (RAG) - Revision history

PlumBot: Bot: Creating new article from JSON