# Query claims in natural language with Amazon Bedrock Knowledge Bases

> **Publication**: aitalky (Independent AI Journalism)
> **Canonical Citation URL**: https://aitalky.vercel.app/news/query-claims-in-natural-language-with-amazon-bedrock-knowledge-bases
> **Original Source**: [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/query-claims-in-natural-language-with-amazon-bedrock-knowledge-bases/)
> **Author**: Shreya Pawaskar (undefined)
> **Published Date**: Wednesday, September 30, 2026
> **Category**: PRODUCTS
> **Tags**: AI, AWS ML Blog, products

## Executive Summary
This technical how-to builds a conversational claims assistant on Amazon Bedrock Knowledge Bases that answers natural-language questions with citations.



## Full Coverage
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:

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