AI & Technical question

Teams using Figma’s design-to-code workflow say the output is inconsistent, so they do not trust it for production work. How would you break the problem down across SceneGraph fidelity, component and token mapping, and code generation; what quality metrics or evals would you put in place; and how would you decide which issue to fix first?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Ability to decompose a fuzzy quality complaint into measurable stages and prioritize the highest-leverage fix.

Break design-to-code into three stages: SceneGraph fidelity, component and token mapping, and generated code quality.
Gather concrete failure examples from teams and tag which stage each one traces back to.
Define an eval metric per stage, such as node-to-code mapping accuracy, token match rate, and lint or diff cleanliness of generated code.
Estimate the volume and severity of failures at each stage to see which one is actually blocking production use.
Decide the fix order based on where errors compound, since a SceneGraph fidelity bug corrupts everything downstream of it.
Set a target metric and timeline for the first fix before moving to the next stage.

Insists on stage-level metrics instead of a single vague trust score, since each stage needs a different fix.
Prioritizes SceneGraph fidelity first if evidence shows it is the root cause, since errors there cascade into mapping and code.
Uses real team examples, not synthetic tests, to validate the eval reflects production pain.
Ships a narrow, measurable fix and re-measures trust before tackling the next stage.

Trying to fix code generation quality before confirming the underlying SceneGraph data is accurate.
Relying on anecdotal trust complaints instead of building a repeatable eval.

Chapters of the AI PM course that teach what this question tests.