GraphRAG: How to improve RAG-based LLM Applications with Amazon Neptune

Description: The integration of large language models (LLMs) into data-intensive applications has revolutionized natural language processing, enabling more sophisticated and context-aware functionality. Retrieval-Augmented Generation (RAG) has emerged as a powerful technique to enhance LLMs by providing them with relevant, more recent knowledge that might not have been included in the original training set. However, these basic retrieval methods often rely on unstructured or loosely structured data sources, which can limit the depth and relevance of the information supplied to the model.

This talk introduces GraphRAG, a novel approach that leverages graph databases like Amazon Neptune to improve the retrieval component of RAG-based LLM applications. By using the structure inherent in a graph, we can capture complex relationships and entities more effectively than traditional RAG solutions, providing richer context and more precise information retrieval to improve the performance of LLM-based applications.

Attendees will gain practical knowledge on integrating Amazon Neptune with LLMs—including code samples for data modeling, query optimization, and real-time retrieval—and review examples quantifying these performance improvements. By the end of the session, they will be equipped with the tools and best practices needed to implement GraphRAG in production environments, helping them develop insightful AI-driven solutions in their own projects.

Target Audience: Technical Architects and Developers

Tech Level: 200-300

Host: SID Global Solutions

SIDGS provides comprehensive digital transformation services to enhance digital presence and performance. We collaborate with clients and help them achieve their true potential.