Chunk Is Dead, Long Live the Graph RAG!
As AI applications increasingly rely on large language models (LLMs) for search, summarization, and decision support, one major limitation persists: traditional Retrieval-Augmented Generation (RAG) systems often treat knowledge as disconnected chunks of text, missing the deeper context behind relationships. But what if we could connect the dots more intelligently?
Smarter Retrieval for Smarter AI
In this webinar, we introduce a graph-based approach to RAG that leverages the power of knowledge graphs to represent meaning more holistically. By structuring information as a web of semantic relationships, this method enables LLMs to retrieve and generate content that is not only more accurate but also more contextually aware.
Join Márcia R. Ferreira and Astrid Krickl, Data and Knowledge Engineers at Graphwise, as they walk through the Graphwise Graph RAG framework—an innovative architecture that enhances semantic retrieval using graph-connected data. Through real-world use cases and the Graphwise platform, they’ll demonstrate how this approach bridges gaps left by chunk-based methods and discuss practical implementation challenges often overlooked in AI pipelines.
Discover these key takeaways:
Knowledge Graphs are Everywhere: Fortune 500 companies are using them, are you?
Graph vs Knowledge Graph: Learn what differentiates a Knowledge Graph from a regular Graph.
Enterprise Structure: Find out the secret recipe for Knowledge Graphs and LLMs in an enterprise.
Vector and Graph: What are the differences and benefits of vector-based RAG and a graph-based RAG approach?
And much more!
Register now to unlock the power of Graphwise Graph RAG!
