Your chatbot is not “smart” if it guesses. It’s just confident.
In this video, I explain RAG (Retrieval-Augmented Generation) in the simplest way possible. You’ll learn how AI can pull the right answer from your own documents first, then write a response based on real context, not vibes. RAG is specifically about making a model reference an external knowledge base before generating an answer.
We’ll break it down using one example the whole way:
A “Return Policy bot” that answers from your company docs.
What you’ll learn:
Why LLMs “hallucinate” when they don’t have your data
What tokens are (how AI reads text)
What embeddings are (meaning turned into numbers)
What a vector database does (fast similarity search)
The full RAG flow: Ask → Retrieve → Answer
The top mistakes that break RAG (chunking + retrieval noise)
