xAI PM Execution: Lifting Model Precision 20% in Under a Month with Contractors

A model's precision has to rise by 20% within two weeks to a month, and you have 8 to 10 contractors to help. Walk through how you would get there. ### Clarifying Questions - Does "20%" mean a relative gain on today's precision, or an increase of 20 percentage points? - What does the model decide, and what does a false positive cost the business or the user? - How much recall, or coverage, are we allowed to give up? - Can the model be retrained within the window, and are the contractors domain experts or general labelers? ### Part 1 — Approach How would you plan the work from the first day to the deadline? ```hint Find out where the errors come from Precision rises when false positives shrink relative to true positives. Before choosing a fix, learn what the model's false positives have in common and which fixes fit in the time you have. ``` #### What This Part Should Cover - Pinning down the target: today's measured precision, how it is measured, and the recall floor. - Diagnosing the false positives before choosing what to change. - Ordering quick and slow changes across the window, with checkpoints and a fallback. ### Part 2 — Data What data would you use, and what new data would you create? ```hint Look at what the model says yes to Think about which examples reveal the model's mistaken positives most directly, and which data must stay untouched so the final number can be trusted. ``` #### What This Part Should Cover - Existing sources that expose false positives, such as production predictions, evaluation sets and user or reviewer feedback. - New labeled data to create and how to sample it. - A trustworthy, separate evaluation set that reflects production. ### Part 3 — Team How would you use the contractors? ```hint Budget their hours Estimate how many items the group can label in the window, then decide how much of that capacity goes to measurement, error analysis and training data. ``` #### What This Part Should Cover - Splitting the contractors across roles and shifting them as the work changes. - Guidelines, calibration and quality checks that make their labels reliable. - A daily rhythm that feeds labeled data into model iterations quickly. ### What a Strong Answer Covers - One plan that ties the approach, the data and the team to the deadline. - Honest handling of the precision and recall trade-off, and of how the gain will be verified. - What happens if the target looks out of reach, and how that is communicated to stakeholders. ### Follow-up Questions - After two weeks precision is up 12%, and the remaining gain would cost a lot of recall. What do you do? - Labelers disagree on a third of borderline items. How does that change your plan and your confidence in the metric? - How do you make sure the gain holds in production, not only on your evaluation set?