What is RAG? | Theory and Architecture of Retrieval-Augmented Generation | Video #45 скачать в хорошем качестве

What is RAG? | Theory and Architecture of Retrieval-Augmented Generation | Video #45

Why are standard LLMs not enough for real-world business apps? 🛑 In Video #45 of our LangChain Full Course, we dive deep into the theory of Retrieval-Augmented Generation (RAG). A Large Language Model (LLM) is like a genius who stopped reading the news two years ago—it’s smart, but its knowledge is frozen in time. RAG is the framework that gives that genius an "Open Book" to look up your specific, private, or real-time data before answering a question. In this video, I explain the core architecture of RAG and why it is the #1 most demanded skill in the AI industry today. ✅ In this theoretical deep-dive, we cover: The 3 Key Problems RAG Solves: 1. Knowledge Cutoff: Accessing information beyond the model's training date. 2. Hallucinations: Grounding the AI in facts to prevent "made-up" answers. 3. Privacy: Using your own data without having to retrain or leak it to a public model. The "R-A-G" Workflow: Retrieval: Finding the right chunks from your Vector Store. Augmentation: Adding that context to the user's original prompt. Generation: Letting the LLM produce a response based only on the provided facts. RAG vs. Fine-Tuning: When to use which, and why RAG is often 10x cheaper and faster. Source Attribution: How RAG allows the AI to say, "I found this answer in Document X," building user trust. Why this matters: Understanding the logic of RAG allows you to troubleshoot your AI when it gives a bad answer. Is the problem the Retrieval (not finding the data) or the Generation (the LLM ignored the data)? Once you know the theory, you can build much more reliable systems. #LangChain #RAG #TheoryOfRAG #AIArchitecture #LLM #GenerativeAI #OpenAI #VectorDatabase #MachineLearning #AITutorial #SemanticSearch #TechTrends2026