Learn LLM engineering — for the app you’re actually building.
Strive shapes an LLM-engineering course around what you’re shipping: a chatbot, an agent, a copilot, a backend that calls a model. Provider APIs, structured outputs, evals, cost control, latency. Lessons stream live, and the recall queue keeps the patterns from rotting between sprints.
Demonstration outline — your course is generated around your answers, so module count, depth, and difficulty will differ from this. Across the 7 modules above there are 27 lessons.
Frequently asked
Which language does the course use?
You pick during the wizard — TypeScript or Python, the two ecosystems where the SDKs are first-class. Examples, the eval harness, and the project use that language throughout.
Does it cover RAG and vector search?
Lightly. RAG gets enough coverage to know when to reach for it; for the full retrieval-and-evaluation track, run the wizard on RAG and LangChain instead.
How current are the model and SDK references?
Each generation is fresh, so the course reflects the providers as they stand when you build it. The course also flags the parts most likely to drift — pricing, context windows, model names — so you know where to double-check vendor docs.
Ready to learn LLM engineering?
Tell us where you are today. AI builds your course in minutes — and the daily recall queue makes sure you keep what you learn.