Stop Fine-Tuning LLMs. Build RAG Instead скачать в хорошем качестве

Stop Fine-Tuning LLMs. Build RAG Instead

Stop relying on ChatGPT’s training data. It’s outdated, it hallucinates, and it doesn't know your business data. If you want to move from being a "Prompt User" to an "AI Architect," you need to master Retrieval-Augmented Generation (RAG). In this 10-minute speedrun, we are stripping away the hype. I’m showing you exactly how to build a production-ready RAG pipeline that grounds your LLM in actual data—no fine-tuning required. 🛑 The Hard Truth: Most developers think they need to "train" a model to learn new data. They are wrong. You need context, not weights. ✅ What We Build Today: The Ingestion Layer: How to slice your data so the AI can actually read it. The Vector Database: Setting up Pinecone to store "meaning," not just keywords. The Retrieval Chain: Using LangChain to fetch the right data before the LLM even speaks. 🚀 The Architect's Stack: Orchestration: LangChain / LlamaIndex Vector DB: Pinecone / Weaviate LLM: OpenAI GPT-4 / Claude 3 ⏱️ Speedrun Chapters: 0:00 - The "Vanilla LLM" Trap (Why they lie) 0:45 - Stop Fine-Tuning (The "Context" Solution) 1:30 - RAG Architecture Blueprint (Ingestion - Storage - Retrieval) 3:30 - The Tech Stack (LangChain & Pinecone setup) 4:30 - The Architect Challenge (Ship code or go home) Recommended Next Step: Once you've built this pipeline, you need to understand how to scale it. Watch this next:    • Your AI Career Path Is Wrong: Here's the R...   ⚠️ DISCLAIMER The information in this video is for educational purposes only and does not constitute financial or career advice. All opinions are my own. Always verify code and documentation before using it in a production environment. #AI #MachineLearning #TechCareer #TheCoderTherapist