RAG Has Been Oversimplified - Exploring Complexities in Retrieval Augmented Generation
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the complexities of Retrieval Augmented Generation (RAG) in this 49-minute MLOps podcast episode featuring Yujian Tang, Developer Advocate at Zilliz. Delve into the nuanced challenges developers face when implementing RAG, moving beyond industry oversimplifications. Learn about embedding vector databases, the consensus on large and small language models, and the intricacies of QA bots. Discover critical components of the RAG stack, including citation building, context vs. relevance, and similarity search. Examine RAG optimization techniques, discuss scenarios where RAG may not be suitable, and explore multimodal RAG applications. Gain insights into fashion app development and video citation methods while understanding the trade-offs in LLM interactions.