RAG and Agentic AI Capstone Project
About this course
Demonstrate you have the job-ready skills to design and implement a complete AI system from data to deployment, with this portfolio-worthy RAG and Agentic AI Capstone Project from IBM. You’ll design and build a production-style multimodal RAG system that combines structured data, embeddings, retrieval logic, evaluation strategies, and intelligent workflows into one cohesive, scalable solution. You’ll create and manage structured JSON datasets, generate text and image embeddings, and construct a vector database to power accurate similarity search and metadata-filtered retrieval. As you progress, you’ll implement robust RAG pipelines, apply re-ranking and evaluation techniques, and strengthen response quality using multimodal inputs and systematic validation approaches. You’ll also design a multi-agent recommendation system, integrate tools using the Model Context Protocol (MCP), orchestrate workflow testing, and launch an interactive Gradio chatbot interface. By the end, you’ll have developed an end-to-end generative AI application that demonstrates practical AI engineering expertise, architectural thinking, and production-ready implementation skills.
90/100
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- Who stands behind it
- 35/35
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What you'll learn
- design and implement a multimodal RAG system
- create and manage structured JSON datasets
- generate text and image embeddings
- construct a vector database for similarity search
- implement robust RAG pipelines
- apply re-ranking and evaluation techniques
- design a multi-agent recommendation system
- launch an interactive chatbot interface
Course objectives
- develop an end-to-end generative AI application
- demonstrate practical AI engineering expertise
- strengthen response quality using multimodal inputs
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