If you want to learn:
- What is RAG (Retrieval-Augmented Generation) and how does it work in AI systems?
- What's the difference between RAG and Agentic RAG, and when should you use each approach?
- How can you make AI agents and LLMs more knowledgeable without expensive training?
- What are the practical use cases for building RAG systems in enterprise AI solutions?
- How does retrieval work to augment large language models with external knowledge?
- What are the benefits of RAG for AI applications and generative AI models?
Then this lecture is for you!
This lecture demystifies Retrieval-Augmented Generation (RAG) and Agentic RAG, two transformative approaches in modern AI development. You'll discover how RAG works by dynamically retrieving relevant information from knowledge bases and data sources to augment LLM responses, making AI agents more knowledgeable without costly model training. The session explains the core difference between traditional RAG and Agentic RAG systems, showing how RAG focuses on retrieval and context injection while Agentic AI takes autonomous decision-making further. You'll learn the fundamental RAG architecture: how queries trigger retrieval from databases and vector databases, how retrieved data gets injected into prompts, and how this enables AI systems to provide accurate, real-time answers grounded in external knowledge rather than relying solely on static training data. The lecture covers practical use cases for enterprise AI, from building RAG-powered chatbots to AI assistant applications, and explains how RAG helps prevent hallucinations in generative AI models. You'll understand the RAG pipeline workflow, including how LLMs use APIs and tools to fetch relevant information from multiple data sources, and how Agentic RAG combines the dynamic data retrieval of RAG with the autonomy of agentic systems for advanced AI agent development. Perfect for those building AI solutions with LangChain or similar frameworks, this session provides the intuition needed to implement RAG systems and create more capable, knowledge-enhanced AI applications.