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RAG and Agentic AI Capstone Project

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RAG and Agentic AI Capstone Project

Instructors: Abdul Fatir

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Demonstrate you have the job-ready skills to design and implement a complete AI system from data to deployment.
Transform unstructured text and multimodal data into structured JSON formats using LLMs to drive data-driven decision-making.
Architect multimodal vector databases and multi-agent systems to coordinate specialized agents for high-accuracy recommendations.
Integrate complex AI ecosystems using MCP, configuring servers and clients to build, validate, and scale tool-augmented agents.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your Software Development expertise

This course is part of the IBM RAG and Agentic AI Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from IBM

There are 5 modules in 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.

In this module, you will use LLMs to transform unstructured restaurant descriptions into structured JSON files by designing prompts and extracting predefined attributes. You will apply multimodal LLMs to generate captions from review images and integrate those captions into structured user review data. Finally, you will build a command-line Python interface to browse, add, edit, and delete restaurant records, integrate LLM-powered structuring functions for new entries, and implement file backup mechanisms before saving updates.

What's included

2 videos1 reading4 assignments3 app items5 plugins

2 videos Total 5 minutes

Course Introduction 3 minutes
Project Overview 2 minutes

1 reading Total 2 minutes

Course Overview 2 minutes

4 assignments Total 63 minutes

Graded Quiz: Build a Structured Generative AI Application 21 minutes
Checklist: Structure Text Data with LLMs 16 minutes
Checklist: Process Multimodal Customer Data with LLMs 16 minutes
Checklist: Build a Simple Interactive User Interface 10 minutes

3 app items Total 135 minutes

Lab: Structure Unstructured Restaurant Data with an LLM 45 minutes
Lab: Process Multimodal Data with LLMs 45 minutes
Lab: Build a Command-Line Data Management UI for Restaurant Data 45 minutes

5 plugins Total 23 minutes

Reading: Helpful Tips for Course Completion 5 minutes
Reading: Assignment Overview: Structure Unstructured Restaurant Data with an LLM 5 minutes
Reading: Assignment Overview: Process Multimodal Data with LLMs 5 minutes
Reading: Assignment Overview: Build a Command-Line Data Management UI for Restaurant Data 5 minutes
Podcast: Recap: Build a Structured Generative AI Application 3 minutes

In this module, you will design and implement the retrieval layer of a multimodal RAG system using structured restaurant text data and food images. You will construct multimodal vector indexes, generate text and image embeddings, and build retrieval workflows that combine similarity search with metadata filtering. You will also implement late-fusion techniques to combine and rerank results across modalities, improving the relevance of retrieved outputs. The module follows a step-by-step retrieval pipeline, from index construction to hybrid retrieval and multimodal ranking, with a focus on practical design rather than tool-specific features.

What's included

4 assignments3 app items4 plugins

4 assignments Total 51 minutes

Graded Quiz: Design a Multimodal RAG System 21 minutes
Checklist: Multimodal Vector Index Construction 10 minutes
Checklist: Similarity Retrieval with Metadata Filtering 10 minutes
Checklist: Multimodal Similarity Fusion and Ranking 10 minutes

3 app items Total 135 minutes

Lab: Construct a Multimodal Vector Index 45 minutes
Lab: Similarity Retrieval with Metadata Filtering 45 minutes
Lab: Multimodal Similarity Fusion and Retrieval Ranking 45 minutes

4 plugins Total 13 minutes

Reading: Assignment Overview: Construct a Multimodal Vector Index 5 minutes
Reading: Assignment Overview: Similarity Retrieval with Metadata Filtering 0 minutes
Reading: Assignment Overview: Multimodal Similarity Fusion and Retrieval Ranking 5 minutes
Podcast: Recap: Design a Multimodal RAG System 3 minutes

In this module, you will design and implement a multi-agent recommendation system. You will define specialized agents with clear roles, goals, backstories, and tasks, and integrate them into a coordinated multi-agent workflow. You will then test how multiple agents collaborate to generate restaurant and recipe recommendations from a single user input. Finally, you will build an interactive chatbot interface using Gradio to expose the system. The chatbot will process user queries, display coordinated agent outputs, and support basic database editing functionality within the interface.

What's included

4 assignments3 app items4 plugins

4 assignments Total 51 minutes

Graded Quiz: Combine Agents into a Multi-Agent System 21 minutes
Checklist: Define Agents and Their Roles 10 minutes
Checklist: Integrate Agents into a Multi-Agent System 10 minutes
Checklist: Build a Chatbot Interface for the Recommendation System 10 minutes

3 app items Total 135 minutes

Lab: Design Specialized Agents for a Recommendation System 45 minutes
Lab: Implement and Test a Multi-Agent Recommendation System 45 minutes
Lab: Build a Chatbot Interface for the Recommendation System 45 minutes

4 plugins Total 18 minutes

Reading: Assignment Overview: Design Specialized Agents for a Recommendation System 5 minutes
Reading: Assignment Overview: Implement and Test a Multi-Agent Recommendation System 5 minutes
Reading: Assignment Overview: Build a Chatbot Interface for the Recommendation System 5 minutes
Podcast: Recap: Combine Agents into a Multi-Agent System 3 minutes

In this module, you will organize agent tools, databases, and documents within an MCP server. You will then build an MCP client and an LLM-based MCP host that communicate with the server and validate the system through testing. You will also design and implement an LLM-powered MCP host with a GUI, enabling the LLM to access server-exposed tools and documents. This module brings together components built earlier into a unified MCP-based system and validates end-to-end tool execution through a GUI-based application.

What's included

4 assignments3 app items4 plugins

4 assignments Total 51 minutes

Graded Quiz: Integrate Agents, RAG, and Tools with MCP 21 minutes
Checklist: Organize Tools and Data in an MCP Server 10 minutes
Checklist: Implement an MCP Client for Server Communication 10 minutes
Checklist: Design an LLM-based MCP Host 10 minutes

3 app items Total 90 minutes

Lab: Build an MCP Server 30 minutes
Lab: Build an MCP Client 30 minutes
Lab: Build a Full MCP Application 30 minutes

4 plugins Total 18 minutes

Reading: Assignment Overview: Build an MCP Server 5 minutes
Reading: Assignment Overview: Build an MCP Client 5 minutes
Reading: Assignment Overview: Build a Full MCP Application 5 minutes
Podcast: Summary: Integrate Agents, RAG, and Tools with MCP 3 minutes

In this module, you will complete your AI capstone project by submitting screenshots of tasks performed in previous labs. You’ll organize and present these artifacts to clearly demonstrate how you designed, built, and integrated structured data, multimodal RAG systems, and multi-agent workflows using LangChain, LangGraph, and MCP. This submission will serve as a final evaluation through an AI-based grading system and provide a portfolio-ready showcase of your end-to-end generative AI solution.

What's included

1 video2 readings1 app item1 plugin

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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Abdul Fatir

IBM

2 Courses 44,843 learners

Tenzin Migmar

IBM

2 Courses 52,642 learners

Offered by

IBM

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Frequently asked questions

This capstone enables you to demonstrate your job-ready skills for advanced roles such as AI Agent Engineer, RAG Systems Developer, MCP Developer, Generative AI Application Engineer, Multi-Agent Systems Developer, and AI Workflow Engineer.

It is ideal for software developers, Python programmers, and AI practitioners who want hands-on experience building and deploying multimodal RAG systems, integrating tools using Model Context Protocol (MCP), and orchestrating multi-agent workflows.

The course is also well suited for professionals reskilling into generative AI engineering roles that require end-to-end system design, tool integration, and production-ready AI application development.

You should be comfortable with Python programming and have a foundational understanding of large language models (LLMs), embeddings, and retrieval-based workflows. Familiarity with RAG concepts and basic agent architectures will help you progress more smoothly.

Completing the earlier courses in one of the related IBM Professional Certificates and specializations is strongly recommended, as this capstone builds on newly learnt concepts and applies them in an end-to-end implementation.

You’ll work with large language models (LLMs) and multimodal LLMs to structure data, generate embeddings, and build retrieval pipelines. You’ll design multimodal vector indexes, implement similarity-based retrieval with metadata filtering, and apply late-fusion ranking techniques. You’ll also develop multi-agent systems using LangChain and LangGraph, build an interactive Gradio chatbot interface, and integrate agents, retrieval systems, and tools using Model Context Protocol (MCP) by configuring servers, clients, and an LLM-based host.

You’ll learn how to structure unstructured text and multimodal data using LLMs, build multimodal vector indexes, and implement similarity-based retrieval with metadata filtering and ranking techniques. You’ll also design and test a multi-agent recommendation system, build an interactive Gradio chatbot interface, and integrate agents, retrieval systems, and tools using Model Context Protocol (MCP) to create an end-to-end generative AI application.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.