Retrieval-Augmented Generation (RAG) is a method used to improve the accuracy of large language models (LLMs) like ChatGPT. Here’s how it works
Finding Information: When you ask a question, the system first looks through a large collection of documents or a database to find relevant information.
Creating an Answer: Once the relevant information is retrieved, the generative model (typically an LLM) uses this information to generate a more accurate and contextually appropriate response.
Retrieval-Augmented Generation (RAG) is a concept within the broader field of machine learning. It combines two machine learning techniques: information retrieval and natural language generation. Here’s how it fits into machine learning:
Information Retrieval: This involves searching through a database or a collection of documents to find relevant information. It uses algorithms to efficiently and accurately find the best matches for a given query.
Natural Language Generation: This involves using a language model (like GPT-3 or GPT-4) to generate human-like text based on the input it receives. The model is trained on large amounts of text data to understand and produce coherent responses.
By combining these two techniques, RAG enhances the capabilities of language models. It allows them to generate responses that are not only coherent but also grounded in up-to-date and specific information. This makes the answers more accurate and relevant.