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.