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Forward-deployed engineers — customers, decomposition and LLM systems
Forward-deployed engineer loops look like software engineering loops with two extra rounds that filter out most candidates — decomposing a vague customer goal, and handling a customer in a role-play — and for AI companies, a technical deep dive on building LLM systems that work. This lesson covers each round and the numbers you should be able to quote from your own work.
What you'll be able to do
- Describe a typical forward-deployed engineer loop and why preparing like a standard engineer fails it
- Decompose a vague customer goal into a workflow, a data inventory and a first milestone, without drawing boxes first
- Handle a customer role-play by finding the real goal and offering a scoped option
- Diagnose an LLM system that gives wrong answers or is too slow, and make an agent's risky tool safe
Before this: what-changed
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Forward-deployed engineer loops look like software engineering loops with two extra rounds that filter out most candidates — decomposing a vague customer goal, and handling a customer in a role-play — and for AI companies, a technical deep dive on building LLM systems that work. This lesson covers each round and the numbers you should be able to quote from your own work. This walkthrough runs about 18 minutes, with runnable code you can edit and re-run as you read. A new account gets 7 days of Pro free, no card required.
Log in to continueThe first module of every track is free to read on the web: see what's open in AI interviews.