The process of optimizing the output of a big language model is known as Retrieval-Augmented Generation (RAG), and before generating a response, it refers to an authoritative knowledge base outside of its training data sources.Instead of depending just on training data, RAG connects LLMs to other data sources so they can get pertinent information at query time.By adding real-time, context-aware input from an external data source, RAG enhances a language model’s output. When a user submits an inquiry, the system first activates the retrieval model, which finds and “retrieves” pertinent information from semantically similar texts, databases, or other sources using a vector database. After that, it transmits the results to a generative AI model, which incorporates the new data into its own model, along with the initial input prompt.Instead of just depending on the model it was trained on, this enables the LLM to generate more precise, context-aware responses based on enterprise-specific or current data.
