AI code diff review practice exercise for product managers

The scenario

A product manager at a B2B SaaS company used an AI coding tool for the first time to improve a small feedback widget that had been working fine for months. His first two prompts went smoothly: one asked the tool to explain how the form submission worked, the other asked for a loading state on the submit button, and both produced clean, reviewable diffs he accepted with confidence.

The third prompt asked for a live character counter below the textarea, turning red near the limit. The counter worked exactly as described. While implementing it, the tool also restructured how the submission logic was organized, extracting it into a named function and changing how the result of the save call was handled. The diff looked reasonable on a quick read. He accepted it.

The next day, three users reported that the form showed a success message but their feedback never appeared anywhere. Every submission since the change had been silently failing to save, with no error visible anywhere in the interface. The character counter code itself was correct and had nothing to do with the bug. The actual cause was buried in the refactored submission logic, in a change that looked like ordinary cleanup.

What you are asked to do

The exercise moves through five connected steps:

• Identify the exact line in the diff that caused submissions to stop saving, describing the mechanism, what the new code does differently at runtime, rather than restating the symptom that data was not saving.
• Explain, in one paragraph, why the AI tool made that specific refactoring choice, what it was optimizing for, and what context it would have needed to recognize the change as risky in this specific case.
• Rewrite the original character counter prompt twice: once constraining exactly what the tool may and may not touch, and once giving the tool the context it needed to make a safe decision on its own.
• Write the exact prompt needed to fix the live regression without breaking the character counter or loading state that were both added correctly.
• Write a five item personal checklist of specific questions to ask before accepting any AI-generated diff, based on what would have actually caught this specific bug.

What a strong answer covers

The exercise's own objectives are the standard a strong submission has to meet:

• Does the bug identification name the specific runtime mechanism, what happens differently when a user submits and the page changes state, rather than just restating that data stopped saving?
• Does the explanation of the AI's choice describe what it was optimizing for, rather than treating the change as simply a mistake with no underlying logic?
• Do both rewritten prompts actually differ in approach, one by restriction and one by added context, rather than being the same prompt phrased two ways?
• Does the fix prompt name the specific function that needs to change and include a concrete constraint against reintroducing the same pattern?
• Are the five checklist items specific, checkable questions rather than generic principles like "review the code carefully"?

Skills this exercise practises

Reading an AI-generated diff for what it does at runtime, not just whether it looks clean on the page. Recognizing that a technically correct, isolated feature can still break something unrelated it was never asked to touch. Writing prompts that either constrain scope explicitly or supply the context an AI tool needs to avoid a risky change on its own. Building a personal review habit specific enough to actually catch the next regression. These map onto the bootcamp's own curriculum on debugging and refactoring with agents, testing and quality guardrails, and preventing what the course calls AI spaghetti. For the deeper systems version of this same discipline, the Claude Code operating model exercise from Building with Claude Code has you design the acceptance criteria and quality gates that would catch this kind of regression automatically. For a comparable tool in a different editor, see how to build a prototype with Cursor, and for a wider view of what else belongs in a PM's AI toolkit, best AI tools for product managers.

Which bootcamp this comes from

This exercise is the practical challenge from Claude Code for Product Managers, a one week, two live session bootcamp on Builders Camp covering Claude Code setup, spec-to-implementation prompts, debugging with agents, and team standards and safety. Completing the practical challenge counts toward the bootcamp's completion requirement and its certificate, alongside the certification quiz.

Builders Camp runs this bootcamp both live and self-paced, included with the Builders Camp Membership alongside every other bootcamp, track, and masterclass.