What is Retrieval Augmented Generation (RAG)?

Retrieval Augmented Generation (RAG) is a technique that enhances the performance of language models by integrating external knowledge sources into the generation process. This method allows the model to retrieve relevant information from a database or knowledge base during text generation, which helps to provide more accurate and contextually rich responses.

By combining the generative capabilities of language models with retrieval mechanisms, RAG effectively augments the model's innate knowledge with up-to-date or specialized information that may not be part of its training data. This makes the model more robust and capable of handling a broader range of queries, particularly those requiring specific or factual knowledge. The incorporation of external information helps it maintain coherence and relevance in generated text, addressing limitations often encountered in purely generative approaches.