Multimodal RAG with GPT – Build Smarter Search & AI Systems

About this course

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course equips you with the skills to build smarter AI-driven systems using Retrieval Augmented Generation (RAG) and multimodal technology. You'll dive into the principles behind RAG and how it powers systems like advanced search engines, chatbots, and recommendation systems. The course will provide hands-on experience, enabling you to create multimodal systems that utilize images, text, and other forms of data to provide more intelligent and context-aware solutions. Starting with foundational knowledge, you will explore RAG systems, their components, and benefits. The course delves into how search capabilities can be integrated into multimodal systems and why this approach enhances both search and recommendation functionalities. You'll build multimodal search systems, creating embeddings and setting up a robust workflow to integrate different data types. You will also gain expertise in constructing a multimodal recommender system that combines RAG with GPT. As you progress, you will experiment with embedding images and using them in a vector database, setting up end-to-end systems, and refining them using hands-on lessons. Furthermore, you'll add a user interface to your multimodal recommender system, creating a polished, interactive tool that can be deployed for real-world use. By the end, you will have built a comprehensive multimodal RAG system with a recommender engine, capable of delivering highly relevant results. This course is ideal for AI enthusiasts, software developers, or data scientists looking to deepen their understanding of advanced search systems, recommendation algorithms, and the application of RAG in multimodal environments. A basic understanding of programming and machine learning concepts is recommended, and the course is suitable for intermediate learners.