Discover how to overcome the limitations of traditional Retrieval Augmented Generation (RAG) and unlock superior AI performance.

Many companies dive into LLMs and RAG architectures expecting full automation of complex information management, only to find their "solutions" aren't effective. 

This white paper directly addresses these issues, providing seven compelling cases for integrating knowledge graphs into RAG architectures that not only remedy common problems but also offer significant additional benefits.

It moves beyond current RAG paradigms, which often rely solely on LLMs and vector databases for document search, by advocating for a powerful fusion of symbolic AI (like knowledge models and graphs) with statistical AI (such as Generative AI). This strategic shift ensures your RAG systems leverage rich domain knowledge models for essential contextual information and utilize graphs for efficient, precise access to diverse knowledge bases.

The paper offers a deep dive into Graphwise’s role in providing additional context, linking facts, enabling explainable reasoning, personalizing interactions, fusing structured content, efficiently filtering results, and building intelligent user query assistants. Unlock the full potential of your AI with more accurate, reliable, and context-rich outputs.