Read with intent
Every lesson starts with a decision you will need to make—not a list of terms to memorize.
Education for the work between disciplines
AI education is crowded with tools and tutorials. We teach the judgment that survives when the tools change.
Explore the curriculum01 / Position
A course should leave you with more than notes. It should change what you can take responsibility for.
02 / First field
Built around a simulated engagement. Every module produces evidence you will carry into the capstone.
Intermediate course
Learn to take an ambiguous customer problem from discovery to a deployed, adopted AI system—combining engineering depth, product judgment, and the discipline to own the outcome.
03 / Method
Short instruction. Real artifacts. Feedback at the moment of uncertainty.
Every lesson starts with a decision you will need to make—not a list of terms to memorize.
Exercises produce briefs, system maps, evaluations, runbooks, and working software.
The course assistant explains, challenges, and gives hints using the exact material you are learning.
A capstone makes your judgment visible: what you chose, what changed, and what the system proved.
04 / Courses
Intermediate course
Work where the problem is still unclear.
Learn to take an ambiguous customer problem from discovery to a deployed, adopted AI system—combining engineering depth, product judgment, and the discipline to own the outcome.
Intermediate course
Build the foundation beneath reliable LLM products.
Prompting, retrieval, evaluation, agents, and the production decisions that turn a model call into a system.
Open course
Understand the intelligent world without the jargon.
A clear foundation for using AI confidently, critically, and responsibly in everyday work.
Open course
Honest classroom practice for an AI-rich world.
A concise, practical course for teachers and curriculum designers: lighten preparation, teach honest AI use, redesign assignments, assess real learning, and protect student data.
Intermediate course
Build a production agent one layer at a time.
A hands-on path from a model call to a deployed, evaluated agent across model, harness, tools, skills, and runtime.
Foundations course
A first-principles introduction to modern machine learning.
Linear models through deep networks, with daily problem sets and a capstone project.
Advanced course
The quiet work that decides how good a language model gets.
Source, clean, annotate, and evaluate language data for low-resource and dialect-rich settings.
The founding course
Explore the Forward Deployed Engineer curriculum and the field engagement at its center.
Open the course