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IBM

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coursesFreemium13hAi Agents Workflow Automation
DevelopersAI EngineersStudents
Master AI agent fundamentals, RAG, and LangChain to build intelligent, context-aware applications.

Overview

This course, 'Fundamentals of AI Agents using RAG and LangChain,' is tailored for developers and AI enthusiasts eager to build sophisticated AI applications. You'll dive deep into the core concepts of AI agents, learning how to design and implement them to perform complex tasks autonomously. A key focus will be on Retrieval Augmented Generation (RAG), a powerful technique for enhancing LLMs with external, up-to-date information, significantly reducing hallucinations. The course will extensively utilize LangChain, a popular framework for developing LLM-powered applications, demonstrating practical implementation through hands-on labs and real-world case studies. By the end, you'll be able to create robust, context-aware AI agents capable of interacting with various data sources and APIs, ready to tackle advanced challenges in AI development.

Instructor

Chris Kubecka

Distinguished Chair of Cyber Policy for the European Union Agency for Cybersecurity (ENISA)

Chris Kubecka is an award-winning cybersecurity professional and Distinguished Chair of Cyber Policy for the European Union Agency for Cybersecurity (ENISA). She is also a renowned author and speaker, known for her expertise in critical infrastructure protection and cyber warfare.

Learning Outcomes

  • Design and implement AI agents using the LangChain framework.
  • Apply Retrieval Augmented Generation (RAG) to enhance large language model (LLM) performance.
  • Build context-aware AI applications capable of interacting with diverse data sources.
  • Understand the core concepts and architecture of intelligent AI agents.
  • Evaluate and integrate various tools and APIs with AI agent systems.