Module 6: RAG | Long Context vs RAG - Do You Still Need Retrieval at All

This episode closes out Module 6 by tackling the question that has been getting louder since large context windows arrived. If a model can hold hundreds of thousands or even millions of tokens at once, do we still need all the architecture we just spent this module building? We explore why RAG was never just about fitting text into a small prompt, what retrieval is actually doing that a large context window cannot, and how the shift from compression to curation changes what good RAG looks like today. We cover when long context is genuinely the better tool, when retrieval still matters deeply, and why in most real enterprise systems the best answer is both working together. The episode closes with the argument that RAG is not disappearing. It is maturing. And everything we built in this module is part of that stronger foundation. By the end you will have a clear and honest picture of where these two approaches fit, and why understanding both puts you well ahead of most people working in this space.