1 min lesson
Example workflows you'd build
Match each case in "Example workflows you'd build" to the signal and response that fit it.
Step 1 of 2
Abstract descriptions of "AI-native workflows" win nobody over. Interviewers want to hear specific systems with a baseline, a target and a way to prove the lift. Here are four canonical FDE deliverables and the number each one moves.
Cursor Agent applies a structural change across thousands of files.
Metric: change volume shipped safely, senior review-time saved.
Drive a legacy framework to a modern one, file by file, with the Agent.
Metric: percent migrated, defects introduced per migrated unit.
An AI pass that catches real issues before a human reviewer does.
Metric: reviewer load, escaped defects reaching production.
Generate a fix candidate and context the moment an alert fires.
Metric: time-to-mitigation, on-call toil reduced.
The pattern under all four is identical. You name a baseline, set a target and decide in advance how you'll prove the workflow actually delivered. That last part is the eval and it's what makes the difference between a number you can defend and a vibe.
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Advanced table
Reference table
- Workflow
- Large-scale refactor
- Baseline you'd capture
- Files changed per week today, senior-hours per refactor PR
- How you'd prove the lift
- Golden set of refactor tasks with known-correct outputs; pass rate and review time before vs. after
- Workflow
- Framework migration
- Baseline you'd capture
- Percent of modules migrated after N months of manual effort
- How you'd prove the lift
- Migration velocity per week and a defect-injection rate measured against the test suite
- Workflow
- PR-review automation
- Baseline you'd capture
- Median review latency, count of defects that escaped to prod
- How you'd prove the lift
- Backtest on past PRs with known bugs: recall on real issues, false-positive rate reviewers tolerate
- Workflow
- Incident pipeline
- Baseline you'd capture
- Median time-to-mitigation, manual steps per on-call page
- How you'd prove the lift
- Replay historical incidents; measure suggested-fix usefulness and time saved per page
| Workflow | Baseline you'd capture | How you'd prove the lift |
|---|---|---|
| Large-scale refactor | Files changed per week today, senior-hours per refactor PR | Golden set of refactor tasks with known-correct outputs; pass rate and review time before vs. after |
| Framework migration | Percent of modules migrated after N months of manual effort | Migration velocity per week and a defect-injection rate measured against the test suite |
| PR-review automation | Median review latency, count of defects that escaped to prod | Backtest on past PRs with known bugs: recall on real issues, false-positive rate reviewers tolerate |
| Incident pipeline | Median time-to-mitigation, manual steps per on-call page | Replay historical incidents; measure suggested-fix usefulness and time saved per page |
Every workflow pairs a baseline, a target metric and a concrete proof method.
Because Cursor's product is itself an AI coding agent, "how do you know it worked?" is not a soft question - the eval set is part of what you ship. A refactor that touches 4,000 files is worthless if you can't show it didn't quietly break 40 of them. Backtesting on historical PRs or incidents is the cheapest honest proof you have.
When you cite an example workflow, always attach a metric and a proof method in the same breath. "I'd build an automated PR-review pass; I'd baseline escaped defects and backtest recall against past PRs with known bugs." That one sentence demonstrates the eval-driven rigor the role is screening for.
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Optional practice