Retrieval-Augmented Generation (RAG) has emerged as a powerful technique in natural language processing (NLP), combining the strengths of retrieval-based and generation-based models. While vanilla RAG models have shown significant improvements in tasks like question answering and text summarization, there is a growing need to push the boundaries even further. In this blog post, we will […]