Product design question
A defense customer asks for "an AI assistant for analysts" but cannot clearly describe the day-to-day workflow or failure modes. How would you work with engineers and ML teammates to turn that vague request into a concrete v1 product, including the user task you would target, the human-in-the-loop design, and what you would explicitly leave out?
- Scale AI
- Product design
- Hard
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What this question tests
Ability to convert a vague customer ask into a scoped product with engineers and ML teammates, in a domain where the customer cannot fully articulate their own workflow.
How to approach it
- Start by shadowing or interviewing analysts directly instead of relying on the stakeholder's paraphrase, to observe the actual day to day task and where it breaks down.
- Narrow to one concrete task, for example summarizing a specific report type or triaging a queue of items, rather than a general purpose assistant.
- Work with ML teammates to assess what the model can reliably do today for that task, and where confidence is too low to trust unsupervised.
- Design the human in the loop pattern: where the model drafts and a human approves, versus where it only surfaces information for a human decision.
- Explicitly scope out adjacent asks, for example broader automation or multi step reasoning, as v2 rather than v1.
- Define the success criteria you would validate with analysts before wider rollout.
What a strong answer includes
- Names a specific concrete task chosen from observed workflow, not the customer's abstract framing.
- Shows collaboration with ML teammates to set a realistic confidence bar for automation versus assistance.
- Explicitly defines the human in the loop boundary, for example model drafts, human approves before any action is taken.
- Lists what was left out of v1 and why, showing discipline under a vague mandate.
Common mistakes
- Accepting the customer's vague framing and building a broad assistant with no clear task.
- Skipping direct observation of analysts and relying only on stakeholder interviews.
Likely follow-up questions
- How would you measure whether the human in the loop step is actually adding value or just friction?
- What would change your v1 scope if analysts turned out to do the task very differently from what the stakeholder described?
More product design questions
- You're asked to deliver an enterprise GenAI application on Scale’s platform in 10 weeks for a customer with ambiguous requirements, strict security/compliance review, and multiple stakeholder groups. How would you scope the v1, convert discovery into clear requirements, run testing and pilot rollout, and decide what to cut versus what must ship for launch?Scale AI · Product design · Hard
- A ministry outside the U.S. asks Scale to build a bespoke GenAI application on top of its proprietary data, but end users cannot clearly explain where the workflow is breaking today. How would you run the first client workshops to uncover the real job-to-be-done, select the highest-value use case, define an MVP, and align the client with Scale’s engineering, MLE, and ops teams on scope?Scale AI · Product design · Hard
- Scale forward deploys to understand real workflows before building. If future end-users in a government agency have different needs from the senior sponsor who is funding the project, how would you gather the right feedback, separate core pain points from feature requests, and turn that into a prioritized roadmap for the first release?Scale AI · Product design · Medium
- A ministry outside the U.S. has several candidate workflows for a bespoke AI solution, but its leadership team is not aligned on which problem is highest priority. How would you run discovery and design workshops to identify the best workflow to target, define a measurable success outcome, and decide whether Scale should build an AI application on top of existing models or invest in a custom LLM?Scale AI · Product design · Hard
- A prospective customer wants an agentic or RL data solution but can only describe the desired outcome, not the tasks, feedback signals, or delivery constraints. How would you run discovery, separate must-have from nice-to-have requirements, and turn the conversation into a concrete plan for product, operations, and next customer validation?Scale AI · Product design · Medium
- You need to ship the first agentic workflow product for defense analysts on a controlled network where internet access, model updates, and human review are tightly constrained. What is the MVP, which user/job would you target first, and what tradeoffs would you make among agent autonomy, user experience speed, and security/risk controls?Scale AI · Product design · Hard
More questions from Scale AI
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop