Retrieval-Augmented Generation (RAG) is one of the most practical ways to make AI chatbots more reliable—by letting the model retrieve trusted information first, then generate an answer based on evidence instead of guesswork.
In this video, you’ll learn what RAG is, why it matters, and how the full RAG pipeline works in plain language. We walk through the three key phases—retrieval, augmentation (building the prompt with sources), and generation—and show how citations can make answers easier to verify. You’ll also see common failure points like bad chunking, weak retrieval, too much context, stale documents, and prompt injection, plus what “RAG in production” looks like with monitoring and evaluation.
If you want grounded, up-to-date answers from your PDFs, manuals, or knowledge base—this video explains the core idea and how to do it well.
