[00:02] wrong answers confidently? Do you know why that happens? And that has a name a glitch, it's literally how AI is built. And here's what happened inside. When AI is trained, billions of
[00:15] documents are feed into it. The text got broken into tiny pieces called tokens, into numbers called embedding. Where similar words get a similar number. And [music] all of those get compressed into billions of weight inside the model.
[00:29] Now, here's the problem. The AI never stored the fact. It stored the pattern. >> So, when you ask it something, it doesn't just look up the answers, it when it doesn't know the answer, it doesn't say, "I don't know." It just
[00:42] And [music] that's hallucination. So, here's how we fix this using RAG. Your [music] gets broken down into tokens, crunched into an embedding, and then converted into a number. Now, they are called as
[00:57] vectors. Those [music] vectors get stored into a vector database. Permanent forgetting. And now when you ask the question, the question also becomes the embedding, [music] converted into number instantly, and those numbers get stored
[01:10] in the vector database. And using the cosine similarity, databases [music] the number, pulled out in the milliseconds. [music] And those real matching chunks get injected into a context window right before the LLM
[01:23] answer. Fresh start, verified information, real data. Now, [music] the LLM reads the actual retrieved facts before writing a single word. And that's what called [music] grounding. Same model, same weight, but now it doesn't
[01:36] just guess, it retrieves the data and it answers. No more confident wrong answers, no more hallucination, and that is RAG. [music] And that's how every you like the information, subscribe to Singular on YouTube channel.