In this episode, Andrew Drozdov, Research Scientist at Databricks, explores how Retrieval Augmented Generation (RAG) enhances AI models by integrating retrieval capabilities for improved response accuracy and relevance.Highlights include:- Addressing LLM limitations by injecting relevant external information.- Optimizing document chunking, embedding, and query generation for RAG.- Improving retrieval systems with embeddings and fine-tuning techniques.- Enhancing search results using re-rankers and retrieval diagnostics.- Applying RAG strategies in enterprise AI for domain-specific improvements.
