A good PR review process is resilient to AI "slop" concerns (www.pcloadletter.dev)

A recent opinion piece argues that fears about AI-generated "slop" overwhelming pull request (PR) reviews are misplaced: the problem isn't AI itself but broken review processes. The author contends that if your team already enforces sensible PR discipline—small, atomic changes, active questioning of authors, and consistent quality vetting—AI-assisted code should be no harder to review than human-written code. Rather than blaming AI for producing lots of code, teams should refuse massive PRs, insist authors can explain their changes, and apply the same scrutiny to AI outputs as to any contribution. Practically, the post recommends concrete steps for the AI/ML and engineering community: prompt models to produce small, focused diffs by walking them through problems step-by-step; enforce strict reviews for added dependencies and versions; and integrate automated dependency/vulnerability scanners (e.g., SonarQube, Dependabot) into the pipeline. The takeaway is operational: strengthen PR hygiene and tooling, not ban AI—good review processes are resilient to AI churn and remain the primary defense against both human and machine-generated sloppy code.