Understanding Retrieval-Augmented Generation

LLMs, being widely used for NLP tasks, are trained on large datasets of images, text, and code that can generate text, analyze images, creative writing, logical reasoning, and many such complex tasks in a very efficient way. However, with all their capabilities, there are some limitations when using LLMs. Even after using millions of parameters for generating the response to a prompt, LLMs still lack accuracy in terms of facts and real-world knowledge of a specific domain. LLMs generate generic responses due to a lack of full context of the prompt or the question being asked. To overcome this limitation of the LLMs, retrieval-augmented Generation (RAG) emerged as an efficient technique.

The RAG framework

Retrieval-augmented generation (RAG) integrates an information retrieval source with the LLMs to help them get better context to generate accurate responses for a given prompt. This prevents the LLMs from generating hallucinatedIt refers to the generation of false, inaccurate and illogical responses that are misleading and not based on facts. content or out-of-context information against a prompt. RAG involves a knowledge base that contains information regarding a specific domain. This serves as an additional source of information for the LLM and can be in the form of text files, pdf manuals, video documentation, logs, etc.

Here is a quick review of the RAG process: