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Improving Retrieval Augmented Generation accuracy with GraphRAG

  • Customers looking to enhance generative AI accuracy can use vector-based retrieval systems and the Retrieval Augmented Generation (RAG) architectural pattern.
  • Lettria demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods.
  • Human questions are complex and graphs represent data in a machine-readable format that preserves the rich relationships between entities, leading to a more accurate answer to complex queries.
  • Graphs maintain the natural structure of the data, allowing for a more precise mapping between questions and answers.
  • Lettria conducted extensive benchmarks using GraphRAG, resulting in answers that were 80% correct, compared to 50.83% with traditional RAG.
  • Amazon Web Services (AWS) offers tools and services to build and deploy generative AI applications, including Amazon Neptune, a fully managed graph database service.
  • Implementing GraphRAG with AWS requires domain definition, graph database storage, and developing skills in graph modeling, graph queries, prompt engineering, or LLM workflow maintenance.
  • Lettria provides an accessible and scalable solution to integrate GraphRAG into applications, including simplified ingestion and processing of complex datasets.
  • Managed GraphRAG implementations through Lettria and Amazon Bedrock offer improved question-answering performance, scalability, and flexibility.
  • By incorporating graphs into RAG workflows, organizations can achieve up to 35% improvement in accuracy, leading to more informed decision-making.

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