What you'll learn:
- Design software architectures that integrate AI components like LLMs, embeddings, and vector search into production systems
- Apply four core AI architecture patterns: API-First, RAG, AI Agents, and Human-in-the-Loop
- Make informed build vs buy vs prompt decisions using a structured decision framework
- Avoid the most common and costly AI architecture anti-patterns that teams make in production
Adding AI to a system is easy. Architecting one that holds up in production is not.
Anyone can call an API and get a working demo. But when real users show up, most AI systems start to crack — answers drift, costs spike, data leaks, and nothing in the logs tells you why. The gap between a demo that impresses and a system that survives is the most valuable skill in software today. This course is built to give you that skill.
AI changes a fundamental rule. Traditional software is predictable — same input, same output, every time. AI systems make decisions, interpret, and generate, and the same input won't always give the same answer. That single shift breaks much of what you know about designing reliable systems, and it puts a new set of decisions on your plate — decisions no one trained you to make.
This course teaches you how to make them well. Every concept is anchored to a clear mental model and a real production example, so it actually sticks. No math-heavy theory. No hype. You'll finish with practical frameworks you can bring straight into your next architecture review.
You already know how to build software that works. This course teaches you how to build software that thinks.
