Retrieval-Augmented Generation (RAG) is a technique that enhances generative models by integrating a retrieval mechanism, allowing them to access relevant external information. In a RAG pipeline, a query first triggers a search for pertinent documents, often using a vector database or search index. The retrieved text is then fed into the language model to guide its final response. This approach was pioneered around 2020 and quickly became significant for knowledge-intensive AI tasks. It allowed smaller or general-purpose models to achieve state-of-the-art results by incorporating external facts, addressing issues like hallucinations and outdated knowledge. RAG gained widespread adoption, powering numerous research papers and commercial applications. However, with rapid advancements in AI models and architectures, is RAG still as relevant today?
Limitations of RAG
Despite its strengths, RAG systems introduce several challenges: