Retrieval-Augmented Generation (RAG) is a technique that enhances the accuracy and reliability of large language models (LLMs) by incorporating information from an external knowledge base without the need for retraining the model. It addresses the limitations of LLMs, such as reliance on outdated training data and the generation of unreliable responses. RAG works by retrieving relevant information from an external knowledge store and using it to create domain-specific and up-to-date responses, making the LLMs more trustworthy and authoritative.
RAG offers the following advantages
- It bypasses the expensive training pipeline that is needed for training LLMs to keep them updated with current and reliable facts. This approach allows users to cross-reference the model’s answers with the original content and ultimately trust its responses.
- RAG enables LLMs to produce better, more accurate responses by leveraging external knowledge, thus increasing the model’s relevance and usefulness in various domain specific contexts.
- RAG ensures data security in LLMs through various security controls and measures to ensure that organizations leverage their internal data as context for LLMs, yet maintain a clear distinction from the trained data. The approach to securing sensitive data is considered a practical approach to protecting regulated data and ensuring compliance requirements.