What will you learn in IBM RAG and Agentic AI Professional Certificate Course
Build job-aligned generative AI skills to create RAG, multimodal, and agentic AI applications in just 3 months.
Design modular, reusable AI workflows with LangChain prompt templates and function calling.
Implement efficient RAG pipelines with vector stores (ChromaDB, FAISS) and similarity search.
Develop multimodal AI apps combining text, image, audio, and video using IBM’s Granite, OpenAI Whisper, DALL·E, and more.
Program Overview
Develop Generative AI Applications: Get Started
⏳ 8 hours
- Master GenAI basics, LangChain prompt engineering, and build a Flask web app with structured JSON outputs.
Build RAG Applications: Get Started
⏳ 6 hours
- Learn Retrieval-Augmented Generation fundamentals, design Gradio interfaces, and build RAG apps with LangChain and LlamaIndex.
Vector Databases for RAG: An Introduction
⏳ 9 hours
- Differentiate vector vs. relational DBs, operate ChromaDB, perform similarity search, and build recommendation systems.
Advanced RAG with Vector Databases and Retrievers
⏳ 1 hour
- Implement advanced FAISS retrievers, design end-to-end RAG apps with LangChain and Gradio, and optimize retrieval patterns.
Build Multimodal Generative AI Applications
⏳ 7 hours
- Integrate text, speech, images, and video into AI apps using IBM’s Granite, Meta’s Llama, OpenAI’s Whisper, DALL·E, Sora, Flask, and Gradio.
Fundamentals of Building AI Agents
⏳ 11 hours
- Develop autonomous agents with tool calling, LangChain agents, data analysis, and visualization capabilities.
Agentic AI with LangChain and LangGraph
⏳ 10 hours
- Build multi-agent systems with memory, reflexion, ReAct architectures, and orchestrate collaborative workflows.
Get certificate
Job Outlook
AI Engineers and ML Engineers with RAG and agentic AI expertise are in high demand to build context-aware and autonomous AI solutions.
Roles such as RAG Specialist, Generative AI Developer, and AI Workflow Engineer command salaries in the $100K–$150K range.
Skills in LangChain, vector databases, and multi-agent frameworks open opportunities in tech, finance, healthcare, and enterprise AI teams.
Specification: IBM RAG and Agentic AI Professional Certificate
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FAQs
- Yes, the course assumes a strong foundation in Python and AI concepts.
- Prior experience with machine learning and deep learning is recommended.
- Knowledge of libraries like LangChain, LlamaIndex, and vector databases is helpful.
- Labs and projects are advanced, focusing on autonomous AI systems.
- Beginner AI learners may find the course challenging without prior preparation.
- Yes, the course emphasizes real-world, production-ready AI projects.
- Covers RAG pipelines, multimodal applications, and autonomous agent orchestration.
- Integrates tools like ChromaDB, FAISS, OpenAI Whisper, DALL·E, and IBM Granite.
- Labs simulate enterprise workflows to prepare you for deployment scenarios.
- Provides experience designing scalable AI agents and workflows.
- RAG Specialist.
- Generative AI Developer.
- AI Workflow Engineer.
- Multi-Agent System Architect.
- Salaries typically range from $100K–$150K USD depending on expertise and location.
- Focused on RAG, multimodal AI, and agentic systems rather than generic AI models.
- Covers advanced vector databases, prompt engineering, and agent orchestration.
- Emphasizes hands-on labs with Flask, Gradio, and enterprise-ready integrations.
- Unlike general AI courses, it prepares learners for production-level autonomous AI applications.
- Includes multi-agent collaboration, memory management, and reflexion techniques.
- Limited coverage of cloud-native deployment at scale.
- Focus is on local deployment using Flask and Gradio labs.
- Learners gain understanding of architecture patterns for enterprise integration.
- You can extend projects to cloud platforms after completing the course.
- Recommended for learners with cloud deployment experience to fully utilize production potential.
