Strategy question
Developer feedback says teams can prototype agents quickly with the API, but shipping them to production is still slow and unreliable. How would you diagnose the biggest friction points across the journey from prototype to production, and how would you prioritize fixes across APIs, SDKs, documentation, and model capabilities?
- OpenAI
- Strategy
- Hard
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What this question tests
Tests structured diagnosis of a broad developer experience problem, and the discipline to prioritize across API, SDK, docs, and model capability instead of defaulting to one fix.
How to approach it
- Define production ready concretely: passes an eval suite, handles tool-call errors gracefully, and runs at predictable cost and latency, not just works once in a demo.
- Map the funnel: prototype started, prototype working, integrated into an app, passing internal evals, live with real traffic, and find where developers drop off.
- Pull qualitative signal from forums, support tickets, and churned trial accounts, and bucket complaints into API reliability, SDK ergonomics, documentation gaps, or model limitations like inconsistent tool calls.
- Size each bucket by how many blocked developers it affects and how close they already are to production, not by raw complaint volume.
- Prioritize on blocked-developer count versus engineering effort, weighting model-capability gaps highest since developers cannot work around them with better docs.
- Confirm with engineering whether the top capability gaps, like tool-call consistency, are feasible to fix this quarter.
What a strong answer includes
- Names concrete friction: opaque rate limits, weak retry and streaming error handling, and no built-in eval harness to catch regressions before shipping.
- Separates quick wins, like clearer docs and SDK retry defaults, from harder fixes that need model or API changes, and sequences the quick wins first.
- Proposes instrumenting the funnel with an assumption that only 20 to 30 percent of prototypes reach production, to prove where drop-off actually happens.
- Ties the fix to a measurable outcome, like production-conversion rate, instead of stopping at a qualitative list of complaints.
Common mistakes
- Defaulting to documentation as the fix because it is cheapest, without checking developers actually read it.
- Not separating one-time onboarding friction from recurring production pain, which need different fixes.
- Prioritizing prototype speed improvements that make the production reliability gap worse.
Likely follow-up questions
- How would you define production ready in a testable way?
- Which single fix would you ship first, and why?
- How would you validate this diagnosis before committing engineering time?
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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 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop