What is RAG: Understanding Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) combines external information retrieval with Large Language Models (LLMs) to enhance the relevance and accuracy of generated responses by accessing data beyond the models' pre-trained knowledge. Given the limitations of LLMs in handling external information and the high cost of retraining, RAG provides a solution by employing a retriever to source relevant data from a vector database and a generator to synthesize responses. This system uses techniques like vector embeddings for efficient similarity searches, enabling the retrieval of pertinent information based on semantic similarity. RAG is particularly useful in applications requiring factual accuracy and depth, such as question answering, data-to-text generation, and multimedia understanding. Hybrid search methods combining keyword and semantic vector approaches can optimize retrieval processes. RAG models are increasingly utilized in diverse real-world scenarios, including the creation of chatbots that outperform those relying solely on LLMs.