If you've been wondering what RAG (Retrieval-Augmented Generation) is and why everyone in AI is talking about it, this video is for you! In this video, I'm going to be doing a complete, no-fluff deep dive into the world of RAG. We break down the foundational concepts using simple analogies, debunk the biggest myths (no, RAG is not dead, and massive context windows won't replace it!), and explore the actual architecture behind successful enterprise AI systems. Finally, I'll walk you through the 10 essential RAG patterns you need to master in 2026 to build smarter, faster, and more accurate AI applications. ⏱️ Timestamps: • [00:00] - Introduction to RAG • [01:03] - What is RAG? The Open-Book Exam Analogy • [02:40] - Top 2 RAG Myths Debunked • [04:20] - RAG Architecture & Document Chunking Strategies • [05:40] - Choosing Embedding Models & Vector Databases • [06:56] - The 10 RAG Patterns You Need to Know (Simple, Branched, HyDE, Agentic, Graph RAG, and more!) Orchestration Frameworks: • LangChain: For building context-aware reasoning applications. • LlamaIndex: Excellent for advanced chunking, data ingestion, and multi-modal RAG. Vector Databases: • Pinecone: Managed, scalable vector database. • Weaviate: Open-source vector database. • Qdrant: High-performance vector search engine. • Milvus: Open-source database built for massive-scale AI. • Chroma DB: The open-source AI-native embedding database. Top Embedding Models (2026): • OpenAI: text-embedding-3-large • Voyage AI: Voyage 3 • Hugging Face (Open Source): BGE-large and E5-Mistral Make sure to check out our upcoming lightning lesson on RAG: https://maven.com/p/85ea43/rag-explained-the-architecture-behind-agentic-ai-systems I am hosting a free 30-min Lightning Lesson on Maven, breaking down RAG, the architecture powering most real AI applications: https://maven.com/p/85ea43/rag-explained-the-architecture-behind-agentic-ai-systems Read my blog on RAG: https://aishwaryasrinivasan.substack.com/p/all-you-need-to-know-about-rag-in I am launching Mastering Agentic AI, a 6-week intensive, technical, and project-based bootcamp starting May 30th. And for my YouTube family, I am giving an exclusive 10% discount. Link is in the description. This is not just for software engineers and AI engineers. If you are an AI PM, a PMM, a go-to-market expert, or in any adjacent role building AI products, this is for you too. Being technical is no longer only an engineer's thing. Every week you will be working on real projects in two flavors: coding and SDK-based for engineers, and no-code or low-code for tech leads, PMs, and everyone else. https://maven.com/aishwarya-srinivasan/mastering-ai-agents?promoCode=EXCLUSIVE-YT
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I finished my PhD and was looking to build something based on my research.i was looking to understand RAG better than just assuming it as intelligent orchestration of application layer. Thank you for this video. It really helped me
Glad to have found this today. I've developed an agentic writers assistant and it's supposed to be creatively transparent and use RAG to help the user organize and retrieve ideas for their writing project! This was so helpful! I didn't even realize I was designing an agentic rag system, but frankly for creative writing, a graph rag would be very helpful for the character relationships. I keep stumbling onto complex AI concepts that I don't realize are at the forefront of AI development. And when I research them I discover people are already doing it! Which is nice for me, cause it means I'm on the right track, but also difficult for me as it means I'm studying all the most complex theories!
Amazing teaching skills. You made the core concepts simple and easy to understand.
Thank you for the information. Very helpful.
Awesome!Thanks!
I'm currently working on Building a RAG model and the knowledge you provide in this video covered everything I learnt for 2 months to know about this project,This video is awesome there is no doubt on it.
Great explanation! Specially loved the systematic breakdown of the RAG types!
looks great adding to that Reranking, indexing, fine tuning( loRA and QLora).
I like your mode of Presentation you are 20% faster than reception rate
You have great communication, technical depth, passion to share. Thank you.
Awesome job in explaining RAG. We are building enterprise RAG based systems and I can clearly correlate to everything that’s been mentioned here. It’s so easy to understand for anyone who is not familiar with RAG. Keep producing great videos.
Really worth it
Excellent tutorial 👍
Thank you for the information. This is clearly explained.
Amazing, never thought RAG can be such a Revolutionary Architecture if Evolved and Optimised correctly! 😲
If you would have shown chunking with an example in visual format, I think by that standard this video would have been great
Information packed😀!
Excellent and very informative video. Gave me an insight into different types of RAG implementations and their applicability across different domain use cases
Think of a student during an exam: Without RAG → answers from memory 🧠 With RAG → allowed to open notes 📖 👉 More reliable answers.
You just earned a subscriber. I'm glad to find this treasure in my feed.