ChannelPulse
Designing an LLM feature, and proving it works
The AI PM technical round asks you to design something a model powers — and then to say how you'd know it's good. This lesson gives you a design checklist built around failure modes, a way to choose between rules, classic ML and an LLM, and an eval plan you can describe in two minutes, plus the triage tree for a model-backed feature that's underperforming.
What you'll be able to do
- Design an LLM feature starting from how it fails, with a human in the loop where the cost of a mistake is high
- Choose between rules, a classic model and an LLM for a given problem, and say why
- Describe an eval plan — golden set, rubric, graders, thresholds, online monitoring — in plain terms
- Triage an underperforming model-backed feature without jumping to "switch models"
Before this: product-sense-with-ai
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The AI PM technical round asks you to design something a model powers — and then to say how you'd know it's good. This lesson gives you a design checklist built around failure modes, a way to choose between rules, classic ML and an LLM, and an eval plan you can describe in two minutes, plus the triage tree for a model-backed feature that's underperforming. This walkthrough runs about 17 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.