IBM RAG and Agentic AI

IBM via Coursera Professional Certificate

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Overview

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Enhance your career in the high-growth field of artificial intelligence with advanced skills in generative AI. This program is designed to equip you with advanced techniques and tools such as prompt engineering, Agentic, and multimodal AI integration, enabling you to create sophisticated & context-aware AI applications. This program will teach you advanced AI skills including developing efficient Retrieval-Augmented Generation (RAG) pipelines, integrating multimodal AI for text, image, and audio processing, as well as designing autonomous multiagent systems. The program will not only help you advance your career in AI but also provides a strong foundation for future career development in areas such as advanced data science, deep learning, and AI-driven automation. You’ll also utilize the latest tools used by AI experts, including integrating external tools and APIs with AI agents, orchestrating complex workflows using LangChain and LangGraph, and frameworks like CrewAI, AG2, BeeAI, and Model Context Protocol (MCP). When you complete the full program, you’ll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise. This program is ideal for software developers, machine learning engineers, data scientists, and anyone with Python programming experience who wants to level up their AI engineering game. Enroll today and master the latest generative AI technologies at the forefront of innovation!

Syllabus

Course 1: Develop Generative AI Applications: Get Started
Course 2: Build RAG Applications: Get Started
Course 3: Vector Databases for RAG: An Introduction
Course 4: Advanced RAG with Vector Databases and Retrievers
Course 5: Build Multimodal Generative AI Applications
Course 6: Fundamentals of Building AI Agents
Course 7: Agentic AI with LangChain and LangGraph
Course 8: Agentic AI with LangGraph, CrewAI, AutoGen and BeeAI
Course 9: Build AI Agents using MCP

Courses

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8 hours 39 minutes
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Get ready to power up your resume with the GenAI development skills employers need. During this course you’ll explore core prompt engineering strategies—like in-context learning and chain-of-thought—and create and manage robust prompt templates. Plus, you’ll follow best practices to handle common errors and experiment with different LLMs and configurations to strengthen your outputs. You’ll then dive deeper into LangChain, mastering chains, tools, and agents to create smarter, more responsive applications. Through interactive labs, you’ll build a complete generative AI app using Python that accepts user input and processes it through your backend prompt logic. Plus, you’ll explore web-based interfaces using tools like Flask and Gradio, developing real-time user experiences powered by LLMs. By the end, you’ll have the job-ready skills and demonstrable practical experience employers look for to design and implement full-stack GenAI apps that solve real-world problems. Sound good? Enroll today!
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7 hours 49 minutes
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Ready to boost your AI career by mastering next-level retrieval techniques for intelligent search and summarization? This hands-on course takes you deep into the world of Retrieval-Augmented Generation (RAG), advanced retrievers, and vector databases such as FAISS and Chroma DB. You'll gain the cutting-edge skills businesses need to design and build scalable, high-performance RAG applications that drive smarter search and response capabilities. During the course, you'll learn how to differentiate retrieval patterns, implement similarity search using FAISS, and integrate LangChain with modern UI frameworks such as Gradio. Then, in practical labs and guided projects, you'll get hands-on experience building an end-to-end AI application that retrieves, summarizes, and answers questions in real time. From multi-query and parent document retrievers to semantic vector search and evaluation, this course will give you the skills to improve internal search engines, chatbot accuracy, and content recommendation systems. Enroll today and enhance your portfolio with hands-on experience building AI that understands context—and delivers results.
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6 hours 42 minutes
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Data Scientists, AI Researchers, Robotics Engineers, and others who can use Retrieval-Augmented Generation (RAG) can expect to earn entry-level salaries ranging from USD 93,386 to USD 110,720 annually, with highly experienced AI engineers earning as much as USD 172,468 annually (Source: ZipRecruiter). In this beginner-friendly short course, you’ll begin by exploring RAG fundamentals—learning how RAG enhances information retrieval and user interactions—before building your first RAG pipeline. Next, you’ll discover how to create user-friendly Generative AI applications using Python and Gradio, gaining experience with moving from project planning to constructing a QA bot that can answer questions using information contained in source documents. Finally, you’ll learn about LlamaIndex, a popular framework for building RAG applications. Moreover, you’ll compare LlamaIndex with LangChain and develop a RAG application using LlamaIndex. Throughout this course, you’ll engage in interactive hands-on labs and leverage multiple LLMs, gaining the skills needed to design, implement, and deploy AI-driven solutions that deliver meaningful, context-aware user experiences. Enroll now to gain valuable RAG skills!
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11 hours 24 minutes
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Are you ready to build AI that thinks, acts, and gets things done? In this course, you’ll learn how to design agents that go beyond language generation to reason, take action, and tackle real-world tasks using tools and data.  During the course, you'll explore the foundations of tool calling and chaining with LangChain. You’ll discover how to extend the capabilities of Large Language Models (LLMs) by connecting them with calculators, code, and external data sources. You'll learn how LLMs trigger tool use through LangChain Expression Language (LCEL) and look at manual tool calling for greater control and accuracy. Plus, you’ll explore built-in agents that can analyze data, create visualizations, and run SQL queries using natural language.  To get the most from this course, we recommend that you have Python programming skills, a basic understanding of LangChain, and familiarity with core AI concepts.  Whether you're building a chatbot or a smart assistant, if you’re looking to build the skills to create dynamic, intelligent, and goal-oriented AI systems, enroll today!
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7 hours 41 minutes
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Ready to level up your GenAI skills? Step into the exciting world of multimodal AI, where language, images, and speech come together to build smarter, more interactive applications. In this hands-on course, you’ll learn how to build systems that work across multiple modalities, from creating AI-powered storytellers and meeting assistants to developing image captioning tools and video generation apps. You’ll gain experience with real-world tools like IBM’s Granite, OpenAI’s Whisper, Sora and DALL·E, Meta’s Llama, Mistral’s Mixtral, and Gradio. Plus, you'll explore multimodal search, question answering, and retrieval systems that combine text, speech, and visual data. By the end of the course, you’ll be able to design and build full-stack multimodal AI solutions using Python and frameworks like Flask and Gradio. If you’re looking to gain in-demand skills for building the next generation of AI applications, enroll today and power up your AI career!
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9 hours 16 minutes
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Gain expertise in using vector databases and improve your data retrieval skills in this hands-on course! During the course, you’ll explore the fundamental principles of similarity search and vector databases, learn how they differ from traditional databases, and discover their importance in recommendation systems and Retrieval-Augmented Generation (RAG) applications. You’ll also dive into key concepts such as vector operations and database architecture to develop a strong grasp of Chroma DB's functionality. You’ll gain practical experience using Chroma DB, a leading vector database solution. And through interactive labs, you’ll learn to create collections, manage embeddings, and perform similarity searches with real-world datasets. You’ll then apply what you’ve learned by creating a real-world recommendation system powered by Chroma DB and an embedding model from Hugging Face; an ideal project to demonstrate your understanding of how vector databases improve search and retrieval in AI-driven applications. If you’re keen to gain expertise in using vector databases and similarity searches, both essential components of the RAG pipeline, then enroll today!
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10 hours 25 minutes
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Ready to build intelligent AI agents that can reason, improve, and collaborate? This hands-on course gives you the skills to build agentic AI systems using LangChain and LangGraph in just 3 weeks. You’ll design stateful workflows that support memory, iteration, and conditional logic. You’ll explore how to build self-improving agents using Reflection, Reflexion, and ReAct architectures, empowering your agents to reason about their outputs and refine them over time. Plus, you’ll work on guided labs where you’ll structure agent feedback, integrate external data, and generate context-aware responses through step-by-step reasoning. You’ll then develop collaborative multi-agent systems that coordinate tasks, retrieve relevant data, and solve complex problems using agentic RAG. Plus, you'll gain experience in agent orchestration, query routing, and governance strategies for building robust, scalable AI applications. By the end of the course, you’ll have built working prototypes of agentic systems and gained hands-on skills to design reliable, adaptable agents. Enroll today and get ready to power up your portfolio!
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12 hours 30 minutes
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Learn to build intelligent, autonomous multi-agent systems using powerful frameworks that can plan, collaborate, and execute complex tasks. This course provides a structured approach to designing AI-powered systems using agentic design principles, orchestration strategies, and proven workflow patterns. You’ll explore popular frameworks such as LangGraph, CrewAI, BeeAI, and AG2 (formerly AutoGen), and learn how to select the right one for your needs. You’ll start with LangGraph, applying key design patterns such as sequential flows, routing, and parallelization to structure agent interactions. From there, you’ll move to CrewAI, where you’ll orchestrate agents, tasks, and tools, generate structured outputs using YAML and Pydantic, and extend capabilities with custom functions. Finally, you’ll explore BeeAI for orchestrating agents and workflows, and AG2 for creating multi-agent conversations and role-based collaboration. Through hands-on labs and real-world use cases, you will gain the skills needed to build scalable, maintainable, and efficient AI applications. Enroll today to gain cutting-edge agentic AI skills employers are looking for.
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10 hours 4 minutes
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Get hands-on designing secure, intelligent AI agent workflows using the Model Context Protocol (MCP) in this labs-driven course. You’ll see how AI systems connect to external tools, services, and data sources. You’ll learn how those connections can be designed to stay safe and predictable using structured permissions, user prompts, and validation workflows. And in hands-on labs, you’ll build agents that reason, retrieve information, and carry out tasks while maintaining security and control. You’ll also work with permission enforcement models, JSON-schema-based elicitation, auditing concepts, and real-world security scenarios. You’ll explore how MCP works and why secure design decisions matter in practice. Plus, you’ll break down user requests, shape safe execution flows, and reduce the risk of unintended actions. Finally, you’ll plan and test a complete MCP-driven agent workflow, showing how usability, capability, and security come together in a real implementation. This course is designed for professionals in development, architecture, automation, or AI-powered applications who want hands-on, practical experience building responsible AI workflows.

Taught by

Abdul Fatir, Faranak Heidari, Hailey Quach, IBM Skills Network Team, Jianping Ye, Joseph Santarcangelo, Joshua Zhou, Karan Goswami, Kunal Makwana, Martin Keen, Ricky Shi, Tenzin Migmar, Wojciech 'Victor' Fulmyk and Zikai Dou