IBM RAG and Agentic AI Professional Certificate Course Syllabus

Overview: This professional certificate is a fast-paced, project-driven program designed to equip experienced AI practitioners with advanced skills in retrieval-augmented generation (RAG) and agentic AI. Over approximately 52 hours of structured learning, learners progress from foundational concepts to building multimodal, autonomous AI applications using industry-standard tools like LangChain, LlamaIndex, ChromaDB, FAISS, and IBM Granite. Each module integrates hands-on labs with Flask and Gradio, culminating in a final project that demonstrates production-ready AI system design.

Module 1: Develop Generative AI Applications

Estimated time: 8 hours

• Master generative AI fundamentals
• Apply LangChain prompt engineering techniques
• Design reusable prompt templates
• Build a Flask web app with structured JSON outputs

Module 2: Build RAG Applications

Estimated time: 6 hours

• Understand Retrieval-Augmented Generation fundamentals
• Design interactive Gradio interfaces
• Implement RAG pipelines using LangChain
• Construct RAG applications with LlamaIndex

Module 3: Vector Databases for RAG

Estimated time: 9 hours

• Differentiate vector databases from relational databases
• Operate ChromaDB for embedding storage
• Perform similarity search and retrieval
• Build AI-powered recommendation systems

Module 4: Advanced RAG with Vector Databases and Retrievers

Estimated time: 1 hour

• Implement FAISS-based advanced retrievers
• Design end-to-end RAG applications
• Optimize retrieval patterns with LangChain and Gradio

Module 5: Build Multimodal Generative AI Applications

Estimated time: 7 hours

• Integrate text, speech, images, and video in AI apps
• Use IBM Granite for text generation
• Leverage OpenAI Whisper for speech-to-text
• Generate images with DALL·E and video with Sora

Module 6: Fundamentals of Building AI Agents

Estimated time: 11 hours

• Develop autonomous AI agents with tool calling
• Implement LangChain agents
• Perform data analysis using agent workflows
• Create data visualizations through AI agents

Prerequisites

• Advanced proficiency in Python programming
• Strong background in machine learning and AI concepts
• Familiarity with REST APIs and web frameworks like Flask

What You'll Be Able to Do After

• Design and deploy modular RAG pipelines
• Build and optimize vector database-backed AI applications
• Create multimodal AI systems combining text, audio, image, and video
• Develop autonomous AI agents using LangChain and LangGraph
• Orchestrate multi-agent collaborative workflows for complex tasks