AI & Technical question
How do you design an API?
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What this question tests
Foundational technical product knowledge, understanding API design principles from a PM's perspective.
How to approach it
- Clarify the API's purpose and consumers, internal service to service, or a public third-party developer API, since the bar for stability differs.
- Define the core resources and actions, nouns for resources like User or Order, and standard verbs, GET, POST, PUT, DELETE.
- Design for consistency and predictability, standard naming conventions, clear error codes, and consistent response formats.
- Plan for versioning from the start, e.g. a version prefix in the URL, so changes do not break existing consumers.
- Consider rate limiting and authentication, to protect the system and control access for external developers.
- Define success criteria, developer adoption ease, measured by time to first successful call, and low support ticket volume for integration issues.
What a strong answer includes
- Distinguishes internal versus public APIs, since public APIs need stricter versioning and documentation discipline.
- Names concrete design choices, RESTful resource naming and standard HTTP verbs, showing real technical fluency.
- Plans for versioning and backward compatibility upfront, not as an afterthought after the first breaking change.
- Ties API quality to a measurable developer experience outcome, like time to first successful integration call.
Common mistakes
- Giving a purely abstract answer with no concrete design choices like naming or versioning.
- Ignoring authentication, rate limiting, or error handling considerations.
- Not distinguishing internal versus external API design needs.
Likely follow-up questions
- How would you version this API without breaking existing integrations.
- How would you decide on rate limits for external developers.
- How would you measure whether this API is well designed.
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture