Master Retrieval-Augmented Generation (RAG) from scratch to advanced architectural concepts!
If you are building LLM applications and want to stop AI hallucinations, RAG is the most critical framework you need to know. In this complete, in-depth tutorial, we go far beyond the basics.
We will break down exactly how RAG works, dive deep into the intuition behind Vector Embeddings, and explore Advanced Chunking strategies to ensure your AI systems retrieve exactly what they need, every single time.
What You Will Learn:
– The Core Mechanics of RAG: How Retrieval, Augmentation, and Generation work together to ground Large Language Models in your private data.
– Deep Dive into Embeddings: How text is converted into high-dimensional mathematical vectors and how similarity search actually works under the hood.
– Advanced Chunking Strategies: Why standard chunking fails, and how to use context-aware, semantic, and overlapping chunking to preserve meaning and boost retrieval accuracy.
– Real-World Architecture: Best practices for designing enterprise-grade data platforms for AI.
About the Instructor:
Rahul Anand is a technology executive with over 20 years of leadership experience in global engineering, data, and platform organizations. Holding professional certifications in Cloud Architecture, Data Science, and AI, Rahul specializes in designing scalable enterprise data platforms and applying the next generation of AI and Agentic frameworks.
