All programs.
Twelve courses across AI engineering, prompt and LLM work, retrieval and inference, product, and leadership. Most run as four-to-eight-week live cohorts, capped tightly and reviewed by the instructor. Browse by category, or read the one we’d send you to first — that’s usually AI-101.
AI Engineering Foundations
The vocabulary, mental models, and base-level systems work every engineer working with LLMs is expected to know in 2026.
Building Production AI Systems
Take a working LLM prototype and turn it into a system you can put on call rotation without flinching.
Evals & Observability for LLMs
Stop guessing whether your model changes helped. Build the eval and telemetry stack your team will quietly come to rely on.
Agentic Systems in Production
Beyond a single tool call: building, debugging, and operating agents that take many actions on behalf of users — without setting the building on fire.
Prompt Engineering in Depth
A short, intense course on the discipline that everyone claims is dead and that the strongest engineers in the field actually take very seriously.
Fine-tuning Open Models
When and how to fine-tune an open model, judged against the alternative every senior engineer should weigh: not doing it.
Multimodal Systems
Vision, audio, and document-grounded models in production — the part of the field that has matured fastest in the last twelve months.
Production RAG Systems
RAG is not a tutorial-grade architecture; it is a real distributed system with real failure modes. This is the course for treating it like one.
Inference at Scale
The other half of an AI system: serving models at production cost and latency without sacrificing the quality that made them worth serving.
Designing AI Products
How AI features become products people actually use — and how to avoid the chatbot-shaped trap most teams fall into first.
Leading Engineering in the AI Era
An eight-week program for engineering leaders making real organizational decisions about AI: what to build, who to hire, where to invest, what to refuse.
AI Systems in Regulated Industries
Audit trails, PII handling, model risk management, and the political work of shipping LLM systems inside organizations that have to produce evidence.
Don’t see what you need? Tell us what to build next →
We add roughly two courses a year. Suggestions from working engineers are how we pick them.