A hands-on, production-focused course on building software with large language models — prompting and structured outputs, retrieval (RAG), agents and tools, evaluation and observability, guardrails, and the cost and latency work that separates a demo from a product.
A real tool from the course. Drag the sliders.
A working demo is the easy 80%. The last 20% — reliability, evals, cost, safety — is the actual job.
This course is about that 20%. It assumes you can already get a model to answer; it teaches you to make it answer correctly, cheaply, safely, and the same way every time — at production scale.
Reliable prompting, few-shot and reasoning techniques, and structured outputs (JSON and schemas) you can actually parse and trust in code.
Retrieval-augmented generation, tool and function calling, and agents — connecting a model to your data and letting it act.
Evaluation and observability, guardrails and security, and the cost, latency, and architecture work that makes an AI feature production-ready.
The first two sections are live now; the rest are being written and appear in your dashboard as they ship.
This is a builder's course. You don't need to train models, but you should be comfortable writing software and calling an API. Curious how the models themselves work? The Finguard LLMs course pairs perfectly with this one.
Who it's for
No account, no install. Progress saves automatically in your browser, separate from your other courses.