If you want to be a strong AI engineer in 2026, there are a few fundamentals you can’t hand-wave—and I’m doing a #10DaysofAIEngineering series to teach them. Day one is RAG. LLMs are incredibly capable because they’re trained on massive internet-scale data, but they don’t know your company’s internal docs, systems, or customer data. And that’s exactly where most real-world AI work lives: enterprise workflows where answers must be grounded in private, verifiable information. In this video, I break down RAG (retrieval augmented generation) in the simplest way: don’t rely only on what the model “remembers” from training—retrieve relevant information from your own data sources, augment the prompt with it, and then have the LLM generate the response. That shift is what makes AI usable in production, because you’re reducing guessing and increasing trustworthiness. I also address the “RAG is dead because context windows are bigger” take—no. The hard part isn’t stuffing more text into a prompt; it’s retrieving the right information. Good RAG is real engineering: structuring data, choosing embedding models, using re-ranking, and applying the right design patterns. In enterprise AI, RAG isn’t optional—it’s core infrastructure.