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

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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


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