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LLM Apps need RAG, RAG need Knowledge Graph

The greatest challenge – and opportunity – of LLMs is extending their powerful capabilities to solve problems beyond the data on which they have been trained, and to achieve comparable results with data the LLM has never seen. Retrieval-Augmented Generation (RAG) is a technique to search for information based on a user query and provide the results as reference for an AI answer to be generated. This technique is an important part of most LLM-based tools and the majority of RAG approaches use vector similarity as the search technique. Baseline RAG performs poorly when being asked to holistically understand summarized semantic concepts over large data collections or even singular large documents. To address this, the tech community is working to develop methods that extend and enhance RAG.

Knowledge graphs are the best choice to back your LLM to help ensure accuracy, explainability, and context. With reliable and verifiable knowledge graph boosts LLM accuracy and explainability, offering robust enterprise capabilities like data protection, governance, high availability, scalability, and flexible deployment, which make it a reliable and scalable choice to pair with LLMs that support mission-critical applications.

In this video, we delve in the details of how you can gain Trust in LLM Apps with Knowledge Graphs.

This video is part of the LLM Optimization Series

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