AI hallucination is one of the biggest concerns in generative AI adoption in enterprises. Generative AI finds potential use cases in every industry. However, the underlying large language models require a deep understanding of the business context and domain knowledge to perform specific tasks. This is where the retrieval-augmented generation framework emerges as a superpower. In this blog, we will discuss what LLM RAG is and what you should know before implementing this framework.
What is LLM RAG?
Retrieval-augmented generation is a framework in generative AI that gives large language models the ability to generate more accurate and relevant responses from your business data.
In this framework, you combine a model with your business-specific datasets or domain-specific knowledge bases. When you ask the generative AI application a question, the system retrieves the latest, relevant data from the connected sources and guides the models to generate accurate responses.