Understanding Context and Contextual Retrieval in RAG

Traditional Retrieval-Augmented Generation (RAG) systems can lose crucial information when documents are split into chunks, as the surrounding context is often lost. This can lead to the retrieval of irrelevant chunks and inaccurate responses, even when using advanced hybrid search methods. A technique called contextual retrieval addresses this by using a large language model to generate a helper text for each chunk, which situates it within the original document. This "contextual text" is then prepended to the chunk before embeddings and indexes are created. By preserving the context, this method dramatically improves the accuracy and specificity of the retrieval process.