Vector databases have become a hot topic in the tech world. The appeal lies in their ability to quickly find similar information in unstructured data. However, this hype overlooks its limitations in understanding complex data and connecting the dots in the given information.

Once known as the only solution to overcome AI challenges, vector databases are falling behind in the race for accuracy and reliability.

We set out on a journey to find a better solution, and the results are thought-provoking.

“Which architecture makes more intelligent AI agents: Graph RAG or Vector RAG?”

How does Vector RAG fare against Graph RAG for AI accuracy and reliability? The Tars and CogniSwitch Inc. Team practitioners pitted two AI Agents against each other for complex customer support scenarios in a Show-and-Tell session.

During the webinar, we demonstrated how graph-based AI better modeled complex relationships and provided better responses than traditional vector-based approaches.

We also uncovered some often-asked questions around Graph RAG in real world scenarios:

• How can Graph RAG provide more accurate and contextually relevant responses?
• How does it address generative AI challenges like hallucinations?
• Is purely Graph RAG enough to create the best-in-class AI Agents for customer support?

Key takeaways of the webinar include:

• Understanding Limitations: Learn about the limitations of Vector RAG, including context loss, struggles with relational information, and lack of reasoning capabilities.
• Graph RAG Advantages: The webinar showcases how Graph RAG improved response accuracy by modeling complex relationships and providing contextually rich answers.
• Real-World Applications: The team demonstrated practical examples of Graph RAG outperforms Vector RAG in customer support scenarios, showcasing its reliability and efficiency.

Want to see this RAG showdown for yourself? Check it out here and view our slides on Slideshare.

 

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