Why and How Retrieval-Augmented Generation Improves GenAI Outcomes

Summary Retrieval-augmented generation (RAG) is gaining traction as an effective method to enhance the accuracy and governance of generative AI language models by integrating domain-specific data into the prompting process. This approach mitigates the risk of hallucinations and boosts the reliability of AI outputs, making it particularly valuable for businesses aiming to utilize GenAI responsibly. The research identifies three key architectural strategies for RAG: vector RAG for understanding unstructured data semantically, relational RAG for retrieving precise data from databases, and graph RAG for analyzing intricate relationships in graph databases. These strategies can be employed separately or in combination to suit various data types and applications. The note also provides principles for effective RAG implementation, emphasizing the need to assess complexity, adopt hybrid solutions, and use integrated platforms to ease implementation and minimize operational risks.