Current large language models (LLMs) are held back by the limitations of vector databases. The most obvious of these limitations are seen when the machine produces “hallucinations”. To address this problem and improve LLM accuracy, the RAG approach has been helpful, but not sufficient given the drawbacks of vectors.

That’s why Lettria has developed Knowledge Graphs, providing more context and accuracy in order to stop hallucinations and restore trust in generative AI.

Lettria has built its Knowledge Studio on the GraphRAG approach. By merging the contextual richness of knowledge graphs with the dynamic power of RAG tasks, we provide the environment that LLMs need to more accurately answer the pressing questions in your company.

The result? Precise, relevant, and insightful answers that capture the true essence of your business knowledge.