In specific contexts, AI models require background knowledge. Retrieval-Augmented Generation (RAG) enhances the AI model’s knowledge by retrieving information from a knowledge base and appending it to the user’s prompt, improving the model’s response. “Traditional RAG solutions often fail to retrieve relevant information due to removed context.” In response, a new method called Contextual Retrieval has been introduced, reducing retrieval failure by 49% and when combined with reranking by 67%. The methodology combines TF-IDF encodings, semantic embeddings, BM25, and rank fusion techniques to improve retrieval accuracy, enhancing performance in downstream tasks. The method significantly improves the overall performance of AI models for more effective results.
https://www.anthropic.com/news/contextual-retrieval