The better the LLM, the better the result, but even open-source LLMs give quality results. We have fed the knowledge graph with the same information we used for the RAG.

The original title in full for this piece is: From Better Reasoning to Faster QFS, An LLM Just Can’t Match An SLM-Knowledge Graph Combo ...

The intersection of large language models and graph databases is one that’s rich with possibilities. The folks at property graph database maker Neo4j today took a first step in realizing those ...

The big challenge now is how corporations will leverage these new LLM-driven productivity boosts in a way where the results are accurate, explainable, and governed. Our benchmark report is the first ...

Knowledge graphs enable customization by aligning the LLM’s outputs with the user’s historical data and preferences. This tailoring can make interactions with LLMs feel more personal and relevant.

If an organisation can both guarantee real-time access to data and establish a 360-degree view of that data, then that data can augment the output of an LLM as it’s generating a response.