Query claims in natural language with Amazon Bedrock Knowledge Bases

Claim answers are scattered across adjuster diary entries, repair estimates, police reports, payment ledgers, and scanned attachments rather than one searchable field. A policyholder might ask whether a claim was approved, while an adjuster might need every open auto claim over $10,000 from last month. Both tasks require finding and combining evidence quickly and accurately. Retrieval Augmented Generation (RAG) uses retrieved documents to ground model responses.

Amazon Bedrock Knowledge Bases is the fully managed RAG capability for documents. Amazon Bedrock handles parsing, chunking, embeddings, and vector storage, so you can build a conversational interface that returns cited answers from claim files. This technical how-to uses synthetic claim records and doesn’t describe a production customer deployment. You build a claims assistant that answers natural-language questions with citations by completing these steps: Ingest claim documents and their metadata from Amazon Simple Storage Service (Amazon S3).

Query them in plain language with the AgenticRetrieveStream API. Ask multi-turn follow-up questions. Scope retrieval with metadata filters on attributes such as claim ID and claim type. Add a contextual grounding guardrail to keep answers tied to…