Implementing RAG from scratch using Legacy Langchain | Agentic AI Project | Euron

Sign up with Euron today : https://euron.one/sign-up?ref=940C6863 Project Resource Link : https://euron.one/course/implementing-rag-from-scratch-using-legacy-langchain One Student One Subscription Euron Plus - https://euron.one/personal-plan/aa2904bd-b41c-407a-b912-9dd8c75d5637?ref=940C6863 Call or WhatsApp us at: +91 9019065931 / +91 9771695888. Unlock the secrets to mastering Retrieval-Augmented Generation (RAG) with this step-by-step guide! This beginner-friendly video dives into how RAG works, its real-world applications, and how it can overcome the limitations of large language models (LLMs). Whether you're passionate about Artificial Intelligence, exploring AiProjectImplementation, or considering a CareerSwitchTechnology, this video offers well-structured and easy-to-understand content to help you succeed! Join me as we explore the practical implementation of RAG, from embedding models and vector databases to creating effective AI-powered applications. You'll gain valuable insights into context learning, building efficient systems, and leveraging RAG for custom data use cases—all while learning to code and discovering programming basics. Let’s build a supportive community of aspiring AI enthusiasts together! If you found this video helpful, don’t forget to like, comment, and subscribe. Share this channel with your friends and family to spread the knowledge and passion for TechnologyEducation and ArtificialIntelligence. Let’s embark on this exciting journey into the world of generative AI—starting today! 🚀 #ainews #promptengineering #vectordatabase #naturallanguageprocessing CHAPTERS: 00:00 - Limitation of Large Language Model 01:41 - What is RAG 03:22 - How RAG Works 04:46 - Importance of RAG 05:18 - Context Window Limitations 09:46 - In-Context Learning Techniques 13:32 - RAG Technique Overview 18:40 - Fine Tuning Strategies 23:17 - Core Components of RAG 26:37 - Challenges in RAG Implementation 27:44 - Choosing the Best Orchestration Framework 29:24 - Need for Orchestration Framework 32:33 - Universal Framework Explained 34:49 - LangOps Ecosystem Overview 38:10 - Overview of Langchain 38:50 - Necessity of Langchain 43:10 - Drawbacks of Large Language Models 51:20 - Advantages of Langchain 58:35 - Evolution of Langchain 1:02:05 - Completion Model vs Chat Model 1:06:05 - Transitioning from Legacy to LCEL 1:07:44 - Fastest Evolving Space in AI 1:09:41 - Teaching vs Providing Solutions 1:16:30 - Learning Path for Developers 1:21:17 - Unstructured URL Loader Explained 1:23:33 - Recursive Character Text Splitter 1:23:51 - Creating a Vector Database 1:25:01 - Performing Semantic Search Operations 1:25:51 - Building a RAG System 1:28:12 - Developing a User App 1:29:55 - Final Thoughts on RAG Instagram: https://www.instagram.com/euron_official/?igsh=Z3A3cWgzdjEzaGl4&utm_source=qr WhatsApp :https://whatsapp.com/channel/0029VaeeJwq9RZAfPW9P2l07 LinkedIn: https://www.linkedin.com/company/euronone/?viewAsMember=true Facebook: https://www.facebook.com/people/EURON/61566117690191/ Twitter :https://x.com/i/flow/login?redirect_after_login=%2Feuron712