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

This article details how Nippon India Mutual Fund improved their AI assistant's accuracy using advanced Retrieval Augmented Generation (RAG) methods on Amazon Bedrock. The solution addresses key challenges in AI-powered information retrieval, particularly for large document volumes.

• Enhanced RAG methods included semantic chunking, query reformulation, and results reranking
• Used Amazon Textract to parse complex document structures like tables and graphs
• Implemented multi-query RAG by breaking complex questions into sub-queries
• Utilized Amazon Bedrock's reranking models to improve result relevance
• Achieved 95% accuracy improvement and 90-95% reduction in hallucinations

The solution demonstrates how advanced RAG techniques can significantly enhance the accuracy and reliability of AI assistants by using sophisticated document parsing, query processing, and result evaluation methods.