Can you review my experience with AI code review tools

Ai Tools
1

I recently used a few AI code review tools on a medium-sized project, and I’m not sure if the feedback they gave me was actually helpful or just generic. Some comments caught real bugs, but others seemed to slow me down without adding value. Can anyone explain how to properly evaluate AI code review feedback, decide what to trust, and integrate it into an existing development workflow so it actually improves code quality and doesn’t just create noise

2

I’ve had a similar exp with AI code review on mid sized stuff, here is how I treat it now:

  1. Sort comments into three buckets
    • Hard bugs and security
    • Style / nitpicks
    • Useless noise

If the tool does not catch real bugs or security issues at least weekly on that project size, it is not pulling its weight.

  1. Look at hit rate
    Example from my last project, TS + Node:
    • ~120 AI comments
    • ~25 were real issues
    • ~40 were ok suggestions
    • ~55 were noise or blocked me

So about 20 percent high value. I tuned it until noise dropped under 30 percent. Before that, it felt like yours, slowed me down.

  1. Configure the rules hard
    • Turn off “rewrite for style” stuff unless you are in refactor mode
    • Disable rules that clash with your linter or formatter
    • Keep only security, null handling, race conditions, resource leaks, obvious complexity problems

If it suggests code that fights ESLint or Prettier, I ignore it by default.

  1. Use it at the right stage
    • Early spike or POC: skip AI review, it slows you
    • Before merge: run AI review once, scan for real bugs, ignore style
    • Legacy code: use it to spot risky areas, then do manual review where it complains the most

  2. Treat it as a junior dev
    • It is good at “did you check for null here”
    • It is weak at architecture, business rules, tradeoffs
    • If a suggestion touches design, I assume it is wrong until proven useful

  3. Watch for these red flags
    • Repeatedly suggests patterns that do not match your codebase
    • Comments explain basics you already know, like “you should handle errors” without context
    • It rewrites working code only for “clarity” with longer solutions

If you see those more than, say, 30 to 40 percent of the time, narrow its scope or turn it off for that repo.

  1. How to decide if it helps you
    For one week, track:
    • Time spent reading AI comments
    • Number of bugs it helped catch before merge
    • Number of times you had to rework code due to AI noise

If bug count saved is low and time cost is high, use it only on risky files, not whole project.

Short version:
Use it for:
• Security, null checks, off by one, missing awaits, thread safety
Avoid it for:
• Style fights, big refactors, architecture suggestions

Treat the comments as hints, not rules. If you feel slower, narrow the config or run it only on changed files before merge, not on every push.

3

You’re not crazy, what you’re describing is exactly the “AI review valley of annoyance” a lot of us hit on mid-sized projects.

I mostly agree with @himmelsjager’s breakdown, but I think there’s one missing angle: integration with your existing process matters more than the raw hit rate.

A few different levers you can pull:

  1. Use it against your own history
    Instead of asking “was this comment helpful,” compare it to what actually broke later.

    • Grab a few recent PRs where bugs slipped through and were fixed later
    • Re-run the AI review on the old code
    • Check: would it have caught those real-world bugs, or is it just hypothetically useful?
      If it is not flagging issues that have actually bitten you in prod, then the “helpful” comments might just be academic.

  2. Tighten the surface area, not only the rules
    People often turn off rules, but leave the tool on the entire repo. Try the opposite too:

    • Only run it on:
      • New files
      • High risk folders (auth, payments, concurrency stuff)
      • Files over X LOC or with high cyclomatic complexity
        That way, even if it is a bit generic, it is “generic” in places where you are likely to miss things during a sleepy review.

  3. Cross check against your own brain, not the linter
    I actually disagree a bit with the “junior dev” metaphor. I treat it more like a unit test generator with opinions.
    When you see a comment, ask:

    • “If this was a human reviewer I trust, would I write a test for this scenario?”
      If yes, add a test, even if you ignore the suggested code change. If no, archive it mentally.
      The value might be in edge-case awareness, not the specific fix it proposes.

  4. Watch for patterns in its “annoying” feedback
    The parts slowing you down are still data. Example patterns I’ve seen:

    • Repeated yapping about missing error handling around one integration
    • Constant whining about a specific shared util being too magical
      That repetition is often telling you “this area is structurally fragile.”
      Instead of micromanaging each comment, schedule a focused refactor on that hotspot.
      So you use the AI less as code-review and more as “heatmap of scary code.”

  5. Time-box the interaction
    The feeling of slowdown is usually you context switching for every comment. Try this:

    • Run AI review
    • Set a 10 minute timer
    • In that time, only:
      • Mark “obviously right” and fix them fast
      • Mark “obviously dumb” and ignore them
      • Add a TODO for “maybe useful, revisit later”
        When 10 mins are up, stop caring. The tool only gets that much of your attention budget.
        If it can’t provide value within that window, it is not worth more of your brainpower.

  6. Measure friction explicitly
    Instead of just “some comments slowed me down,” write down for a couple of PRs:

    • How many times did you scroll back and forth due to its suggestions?
    • How often did you re-run tests only because of AI-induced churn?
    • How often did it cause merge conflicts with in-progress work?
      If the friction per bug caught is too high, you are better off relying on human review plus stronger tests.

  7. Decide the role you want it to play
    Generic-feeling feedback often comes from trying to let it be reviewer, linter and senior engineer at once. Pick one role:

    • Edge-case / safety checker: focus on input validation, nulls, async, concurrency
    • “What did I forget” copilot: only run on large PRs before merge
    • Legacy spelunker: only run on old, scary files
      Everything outside that chosen role is “out of scope” and auto-ignored.

In your situation, I’d literally do a 1 week experiment:

  • Turn off AI review on most of the codebase
  • Keep it only on: risky modules + pre-merge step
  • Time-box to 10 minutes per PR
  • After that week, list: “Bugs prevented,” “Annoying churn caused”

If that list looks thin on the “prevented” side and long on the “churn” side, treat the tool as optional tooling you call manually on specific files, not something that auto-comments on every commit.

You’re allowed to say “this is net negative for this project shape” and only turn it back on when you are doing a scary refactor or touching auth/payments. It is a tool, not a manager. If it makes you feel like you are fighting for control of your own code, that is already a pretty loud signal.