Improving RAG Answer Quality Through Complex Reasoning

Explore how multi-hop retrieval can enhance the quality of answers in Retrieval-Augmented Generation (RAG) systems, particularly in complex reasoning tasks. Using DSPy and Indexify, the post demonstrates the construction of a question-answering chatbot for the healthcare domain. The setup includes the installation of necessary packages, data ingestion, and creating multi-hop retrieval logic for efficient question handling. The integration allows for dynamic context generation, deduplication, and chain-of-thought reasoning, showcasing significant improvements in handling complex queries.