AI Engineering
Explained Properly
Not hype, not magic: concrete mental models, real protocols, and working code. 9 tracks for every role, from curious beginner to production SRE.
Learning Tracks
9 tracks, 73 modules published. Start anywhere.
Track 1
Foundations
How LLMs work, what they can and can't do, and the concepts that everything else builds on.
Track 2
Retrieval-Augmented Generation
Connecting LLMs to your own data โ from basic pipelines to advanced retrieval architectures.
Track 3
Protocols & Integration
MCP, A2A, AG-UI โ the standards that let AI systems communicate, delegate, and interoperate.
Track 4
Agent Architecture
Building multi-agent systems that work reliably โ orchestration, failure modes, and production patterns.
Track 5
Infrastructure & Serving
Running AI workloads reliably and cost-efficiently โ from local models to production serving.
Track 6
Evaluation & Observability
How to know if your AI system is working โ evals, tracing, cost management, and CI/CD gates.
3 more tracks