Large Language Models (LLMs) have revolutionized natural language processing, but challenges with accuracy and explainability persist. This talk introduces GraphRAG, an innovative framework that integrates Knowledge Graphs (KGs) with LLMs to address these challenges. By leveraging the rich, structured data from KGs, GraphRAG enhances LLM outputs, providing robust context and significantly improving the precision of generated information.

This integration not only increases factual accuracy but also boosts model interpretability, making AI systems more reliable and transparent. The session will demonstrate how GraphRAG uses the interconnected nature of KGs to ground LLMs in real-world knowledge. Attendees will see practical examples and performance evaluations that illustrate how this approach mitigates common limitations of traditional LLMs, such as ambiguity and information gaps.

By showcasing the transformative impact of combining Graph Technology with LLMs, this session highlights advancements in both the effectiveness and clarity of AI responses, setting a new standard for AI-driven systems.

Target Audience: This session is primarily aimed at DeepTech Professionals and AI Researchers, as it focuses on advanced techniques to improve the accuracy and interpretability of LLMs using Knowledge Graphs. Data Scientists and Software Architects are the secondary audience, as they will benefit from understanding how Graph Technology can be applied to enhance AI systems, creating more reliable and transparent models for real-world applications.