How Nippon India Mutual Fund improved the accuracy of AI assistant responses using advanced RAG methods on Amazon Bedrock

This post is co-written with Abhinav Pandey from Nippon Life India Asset Management Ltd.

Accurate information retrieval through generative AI-powered assistants is a popular use case for enterprises. To reduce hallucination and improve overall accuracy, Retrieval Augmented Generation (RAG) remains the most commonly used method to retrieve reliable and accurate responses that use enterprise data when responding to user queries. RAG is used for use cases such as AI assistants, search, real-time insights, and improving overall content quality by using the relevant data to generate the response, thereby reducing hallucinations.

Amazon Bedrock Knowledge Bases provides a managed RAG experience that can be used for many use cases. Amazon Bedrock Knowledge Bases is a fully managed service that does the heavy lifting of implementing a RAG pattern—including data ingestion, data chunking, data embedding, and query matching. Amazon Bedrock offers a choice of high-performing foundation models (FMs) from leading AI ...