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Discover how to achieve a significant improvement in answer quality in AI projects that leverage Retrieval Augmented Generation (RAG).
Common RAG solutions are known to combine the strengths of retrieval-based methods and generative models to use the power of generative AI with company data.
However, many of them fail to deliver the required accuracy and relevance of generated text as they consist of standard components and architecture.
By leveraging advanced techniques like knowledge graphs, you can greatly surpass basic RAG implementations. This approach not only enhances performance but also makes the specific quality of the LLM less critical and reduces hardware requirements, optimizing both results and resource utilization.
