AI product sense and scoping
- System design
- Asked often
- About 1 h 30 min to learn
- 2 vetted resources
What this topic covers
Product-sense and customer rounds, common for applied AI and forward-deployed roles, test whether you can scope an AI feature before you design it. Interviewers want to hear you ask what job the user is really doing and what a wrong answer costs them, then decide honestly whether a rule, a search box or a form beats an LLM. A strong answer weighs false positives against false negatives for this user, chooses between automating and assisting, and proposes layered metrics: one north-star outcome, a few quality and cost metrics, and countermetrics that catch harm. It ends with the smallest version that delivers real value and a plan to learn from it.
You’re ready when you can
What interviewers check, from the resources below.
- Can name a case where a rule or plain search beats a model, and say why
- Chooses between automating and assisting based on the user and the stakes
- Weighs false positives against false negatives for a specific user, not by default
- Can propose a success metric plus a countermetric that would catch harm
- Can argue for launching a heuristic or non-LLM baseline first
- Instruments metrics before deciding what the model should optimise
- Picks a simple, observable proxy metric and says what it misses
What to study, in order
Each link opened and checked by hand; the date says when.
Start here
User Needs + Defining Success (opens in a new tab)
People + AI Guidebook (Google PAIR) · 40 min · Free · checked 21 Oct 2026
- Why
- Product-sense rounds reward candidates who question whether AI belongs in the feature at all, and who price the cost of being wrong. This chapter is a clear framework for both.
- What to read
- Read all three parts: “Find the intersection of user needs & AI strengths” (especially “Decide if AI adds unique value”), “Assess automation vs. augmentation”, and “Design & evaluate the reward function” with its sections on false positives and negatives and precision versus recall. Then apply it to one LLM feature you’ve built.
- What to skip
- Skim the Summary and References; the worksheets linked from the chapter are optional.
Free alternative
Rules of Machine Learning (opens in a new tab)
Google for Developers · Martin Zinkevich · 30 min · Free · checked 21 Oct 2026
- Why
- Written for classic ML, but the opening rules are the heart of scoping any AI feature: ship the simple version, measure everything, and only then add a model.
- What to read
- Read “Before Machine Learning” (Rules #1 to #3) and the rules on choosing a first objective in “ML Phase I: Your First Pipeline” (around #12 and #13). Translate each into an LLM product: what’s the heuristic baseline, and what simple metric proxies the real goal?
- What to skip
- Skip “ML Phase II: Feature Engineering” and “ML Phase III”; they’re about tuning ranking models, not scoping.
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