Building Multimodal AI with RAG & Context Engineering

About this course

This course contains the use of artificial intelligence

Disclosure: AI tools were used only to assist in creating the course outline and course thumbnail. All instructional content, explanations, and project walkthroughs were fully created manually by the instructor.

Welcome to Building Multimodal AI with RAG & Context Engineering course. This is a comprehensive project based course where you will learn how to create and design a multimodal AI system that is capable of processing different types of inputs such as text, images, and audio files. This course is a perfect combination between Python and multimodal AI, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in artificial intelligence development. In the introduction session, you will learn the basic fundamentals of multimodal AI, such as getting to know how it works, how Retrieval Augmented Generation is used to provide multimodal AI systems with access to relevant knowledge and information beyond their training data, and how context engineering is needed to organize and structure information before it is provided to a multimodal AI system. Then, in the next section, you will learn how to connect your system with large language model API using OpenRouter, Mistral AI API, and Google AI Studio, specifically, you will learn how to create API key and integrate API key, enabling your system to interact with AI and generate responses, summaries, insights, recommendations, and other intelligent outputs based on user inputs. Afterward, we will learn how to incorporate a vector database into our system, in this case, we will use Chroma to store and retrieve embeddings generated from text, images, and other data sources. By doing so, we enable the large language model to access relevant information from an external knowledge base, helping it generate more informed and context aware responses. Before starting the projects, we will learn about basic RAG concepts such as performing semantic sear

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