Foundations of AI Engineering

Artificial intelligence is transforming the software landscape, but building reliable AI systems requires a solid engineering foundation. This text-based course guides you through the essential concepts of AI engineering, preparing you to design, build, and evaluate intelligent applications. You will start with key terminology and foundational definitions, ensuring you have a strong grasp of how machine learning models function. As you progress, you will explore modern AI design patterns, including vector databases and retrieval-augmented generation (RAG), which are essential for building context-aware applications. What you'll learn: 1. Understand foundational AI concepts, machine learning paradigms, and neural network architectures. 2. Design effective prompts using structured prompt engineering techniques. 3. Explore vector databases and how they store and retrieve high-dimensional data. 4. Integrate pre-trained large language models and APIs into software workflows. 5. Apply basic MLOps principles for deploying, testing, and monitoring AI systems. 6. Practice evaluating AI model outputs for accuracy and performance. This guide starts with basic definitions and gradually builds up to practical architecture patterns and deployment strategies. It is designed specifically for beginners, software developers, and technology enthusiasts who want to understand AI systems without needing a background in advanced mathematics. Begin your journey into AI engineering and learn how to build the next generation of software.