Retrieval-Augmented Generation (RAG)

I will briefly explain how RAG works with Large Language Models (LLMs).

RAG is a technique that combines information retrieval and language generation to create responses that are both factual and relevant to the user’s query.

It involves retrieving relevant information from a knowledge base (database, full-text search, vector database, others) or external data source (APIs) and then using that information to generate a response that is tailored to the user’s specific needs.

It does so by using the retrieved information to augment the prompt for the LLM.

RAG is that simple, you use the information returned by knowledge base or external data source in the prompt.

scriv.ai