TL;DR: Turn ambiguity into structure: clarify the real goal, write your assumptions down, ship a small first version that tests the riskiest one, and adjust as signal arrives, communicating the whole way. Use STAR, say "I," and show forward motion instead of waiting for perfect requirements.
The move that separates strong answers from weak ones is sequencing: validate the riskiest unknown before you build the expensive thing.
How to approach it. This screens for comfort and effectiveness when the problem is not handed to you fully specified, which is most real applied-AI work (labs probe it by deliberately under-specifying the interview problem itself). Pick a story where requirements were genuinely unclear and you drove to clarity. Structure it STAR with the weight on how you created structure, not on the final result.
A strong answer. Walk the arc with a real example:
- Situation: real ambiguity. "Leadership wanted us to 'use AI to improve support' with no defined metric, scope, or data." Make it clear nobody handed you requirements.
- Clarify the actual goal. "I talked to stakeholders and support agents to find the real objective, deflecting repetitive tickets, and proposed a measurable target: deflection rate without hurting CSAT." Converting a vague ask into a metric is the core move.
- Make assumptions explicit and de-risk. "Where I could not get answers, I wrote my assumptions down and flagged the riskiest one, whether our docs even covered the top ticket types, and checked it first." You proceed on stated assumptions and validate the dangerous one rather than freezing.
- Scope a small first version. "Instead of designing the full system, I shipped a narrow prototype on the top three ticket categories to get real signal fast."
- Iterate as information arrives. "The prototype showed retrieval, not the model, was the bottleneck, so I refocused there."
- Communicate throughout. Regular, honest updates on what I knew, what I assumed, and the next step, so stakeholders stayed aligned despite the uncertainty.
- Outcome and lesson. Quantify the result and name the lesson, usually "align on the success metric and the riskiest assumption earliest."
The throughline: ambiguity is not a reason to wait. You impose structure (goal, assumptions, small scope, fast feedback) and keep everyone informed.
| Instinct under ambiguity | What strong candidates do instead |
|---|---|
| Wait for full requirements | Proceed on written assumptions, validate the riskiest first |
| Design the whole system | Ship a narrow prototype to learn |
| Go quiet until done | Share knowns, assumptions, and next step on a cadence |
Key takeaways.
- The signal is structured progress, not a clean problem: clarify the goal, write assumptions, scope small, iterate.
- Test the riskiest assumption first; that is what makes being wrong cheap.
- Pick a story that was genuinely underspecified, or it does not demonstrate the skill.
What interviewers probe next.
- "How did you decide what to build first with so little info?" The smallest thing that tests the riskiest assumption and yields real signal.
- "What if your assumptions were wrong?" They were explicit and cheap to revisit, and I validated the riskiest first, so being wrong was a fast correction, not a sunk project.
- "How did you keep stakeholders comfortable amid uncertainty?" Frequent, honest updates of knowns, assumptions, and next steps.
- (Anthropic and other labs) the interview problem itself is often under-specified; they watch whether you ask clarifying questions and set your own reasonable scope before coding.
Common mistakes.
- A story where the problem was actually well-defined, which does not demonstrate the skill.
- Waiting for perfect requirements instead of proceeding on explicit assumptions.
- Boiling the ocean: designing the full system instead of a small first version to learn.
- No communication thread, so stakeholders were surprised by the direction.