Introduction
Retrieval-Augmented Generation (RAG) is an AI framework that enhances the outputs of large language models (LLMs) by incorporating information from external sources. It combines the generative capabilities of LLMs with the retrieval capabilities of traditional information retrieval. This combination allows RAG to access and reference information outside the LLMs' training data, leading to more accurate, up-to-date, and contextually relevant responses.
How RAG Works
1. Retrieval
A user's query is first used to search an external knowledge base or database.
2. Augmentation
The retrieved relevant information is then integrated into the user's prompt before being sent to the LLM.
3. Generation
The LLM generates a response based on the augmented prompt, incorporating the retrieved context.
Benefits of RAG
Enhanced Accuracy
By accessing external knowledge, RAG can generate more factually correct and up-to-date answers.
Improved Context
RAG allows LLMs to produce responses that are more relevant to the specific user query and context.
Reduced Need for Fine-Tuning
RAG can provide some of the benefits of a custom-trained LLM without the need for extensive training or fine-tuning.