AI Agent Development with LangChain and RAG

As the demand for generative AI solutions grows, the ability to build systems that interact with real-world data has become a critical skill for modern developers. This course provides a clear path for those looking to move beyond simple chat interfaces to create functional AI agents that can retrieve information and perform complex tasks. You will gain a deep understanding of how to connect large language models with external data sources to ensure accuracy and relevance in AI-driven applications. By the end of this course, you will be able to design and implement sophisticated AI workflows that leverage the latest techniques in information retrieval and autonomous logic. You will transform from a curious learner into someone capable of architecting smart systems that solve practical problems. What you'll learn: - Understand the core architecture of large language models and the foundational concepts of AI agents - Master LangChain components to orchestrate complex workflows and manage dynamic prompt templates - Implement Retrieval-Augmented Generation (RAG) to bridge the gap between static models and external data - Apply vector databases like FAISS and modern embedding techniques for efficient information retrieval - Design robust prompt engineering strategies to enhance agent reliability and output quality - Explore agentic patterns and memory management to maintain context during multi-turn interactions The course begins with essential terminology and the mechanics of tokenization and embeddings before moving into the practical construction of RAG pipelines and autonomous agent logic. It is designed specifically for beginners and developers new to generative AI, requiring no prior experience with AI agents. Start building smarter, data-aware AI applications today.