Retrieval Augmented Generation (RAG) is a technique in natural language processing (NLP) that enhances the capabilities of generative artificial intelligence models by combining them with retrieval-based methods. RAG exploits the best of both worlds: the ability to retrieve relevant information from a vast body of data, such as articles, databases or even entire knowledge bases like Wikipedia, and the ability to generate coherent, contextually appropriate responses based on the retrieved information.
What is Retrieval Augmented Generation (RAG)?
How does a RAG work?
- Retrieval phase: When a question is asked, RAG begins retrieving information relevant to the question. This is usually done using a document retrieval system that scans a database of texts to find segments that match keywords or topics related to the query.
- Generation phase: Once the relevant information is retrieved, a generative model (such as those used in traditional language models) takes over. It uses the retrieved texts as a source of knowledge to compose a response that is not only relevant but rich in detail, making it appear informed and accurate.
Why do we use RAG?
The integration of retrieval and generation enables RAG models to produce answers that are more accurate and detailed than those generated by purely generative AI systems. This makes RAG particularly useful for applications where accuracy of information and depth of knowledge are crucial, such as in academic research, customer support and content creation.
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