AI Engineering in 2026 is no longer just about prompts — it’s about building AI Agents, RAG pipelines, and production-ready LLM systems. This course is designed to be hands-on. Instead of just explaining AI concepts, we’re going to install tools, run models locally, and experiment with the systems that power modern AI engineering.
In this course, you’ll move from using tools like ChatGPT to engineering real AI architectures with agents, RAG, structured outputs, and hybrid routing that combine local models, cloud APIs, RAG pipelines, and agentic workflows.
You’ll start by running your own local LLM and validating exactly how it communicates. From there, you’ll build a simple AI assistant and then progressively evolve it into a structured, observable system.
You’ll learn how to:
- Build AI Agents with controlled execution loops
- Implement reliable RAG (Retrieval-Augmented Generation) pipelines
- Enforce deterministic outputs using structured schemas
- Separate interface, engine, routing, and memory into clear architectural layers
- Design hybrid AI systems that combine local and cloud models
- Evaluate AI frameworks based on system design rather than hype
This course is designed as a practical AI engineering course for developers who want to understand what happens between “prompt” and “production” in real-world systems. If you’ve experimented with ChatGPT or LLM APIs and want to move toward building scalable, production-ready AI systems with confidence and clarity, this course is for you.
By the end, you won’t just be using AI tools — you’ll be designing reliable, observable, production-ready AI systems you actually understand.
