Need feedback on AI code review and user review workflow

Ai Tools
1

I’m trying to set up a smooth workflow where an AI handles code reviews and then users review or validate what the AI suggested. Right now the process feels clunky and I’m not sure I’m structuring it the best way. Can anyone share best practices, tools, or examples for combining AI code review with human user reviews so the feedback is accurate, fast, and easy to manage?

2

Been doing AI code review experiments for a while. Here is a structure that tends to work without feeling clunky.

  1. Split the AI review into clear phases

Phase A: Static checks
• Linting hints
• Complexity hotspots
• Obvious bugs, null checks, off by one, unsafe casts
• Security patterns, SQL injection, unsafe deserialization, etc

Prompt idea:
“Act as a senior engineer. Analyze this diff. Output:

  1. Defects (bug risk)
  2. Security issues
  3. Style or readability
  4. Tests that should be added
    Keep it short. Reference line numbers.”

Phase B: Design and architecture notes
Separate prompt. This keeps noise out of small PRs.
“Look at this diff and its context. Comment only on API design, coupling, and long term maintenance risk. If nothing important, say ‘No major design concerns’.”

  1. Force the AI to produce structured output

Have it respond as JSON or bullet structure so your UI can render it clean.

Example JSON:

{
‘summary’: ‘3 findings. 1 high, 2 low.’,
‘findings’: [
{
‘id’: ‘F1’,
‘type’: ‘bug’,
‘severity’: ‘high’,
‘location’: ‘file.js:42’,
‘title’: ‘Potential null dereference’,
‘details’: ‘x is not checked before use.’,
‘suggested_fix’: ‘Add null check before calling foo().’,
‘status’: ‘pending_review’
}
],
‘tests_suggested’: [
‘Add test for null x in bar()’
]
}

This lets you build a “review checklist” UI instead of dumping text.

  1. Make the human review step fast and binary

For each finding, give the user three quick actions:
• Accept and create follow up task or comment
• Reject and mark reason
• Needs discussion

You can store human feedback to fine tune prompts later.

  1. Tight integration with your PR system

Best setup I have seen:
• AI runs on every PR as a bot user.
• It posts a single top level comment with a short summary and links to details.
• Details page or side panel lists AI findings with accept / reject buttons.
• Once user finishes, bot edits its own comment to show “Reviewed by human: 7 accepted, 3 rejected”.

This reduces clutter for other reviewers.

  1. Control when AI runs

Do not run on everything. Use rules:
• Only on PRs larger than X lines.
• Or only when label “ai-review” is present.
• Or when author opts in with a comment trigger like “/ai-review”.

  1. Separate “AI as reviewer” from “AI as assistant”

Two useful modes:

Reviewer mode
• Comments on PR with findings.

Assistant mode
• Developer asks targeted questions.
• Example prompt: “Explain these AI comments like I am a mid level dev and give suggested patches.”

Keep those flows distinct in the UI so people know what they are clicking.

  1. Feedback loop into prompts

If you log rejected findings with reasons, you can refine prompts so the AI stops complaining about the same false positives.

Simple process:
• Track how often each “type” of comment gets rejected.
• If a pattern gets rejected more than say 60 percent of the time, adjust prompt to be stricter about that pattern.

  1. Practical workflow example

Typical PR flow:

  1. Dev opens PR.
  2. CI runs tests and lints.
  3. AI bot runs, posts single summary comment with link to “AI review details”.
  4. Author goes to AI review page:
    • Skims high severity items first.
    • Accepts or rejects each.
    • Optional “apply patch” for simple fixes.
  5. After author pass, human reviewer sees:
    • “AI: 2 open issues, 5 resolved, 3 rejected by author.”
  6. Human reviews only the important stuff, and can ignore the rest.
  1. Guardrails for trust

To avoid people blindly trusting it:
• Tag each suggestion with “confidence: low/medium/high”.
• Ask the AI to self score confidence and require tests for high impact changes.
• Never auto merge based on AI.

If you share what tools you use now, GitHub, GitLab, custom, and how your team sizes look, you can tune this further.

3

What you and @cazadordeestrellas are describing is basically “AI reviewer as a junior dev + checklist generator.” That’s pretty solid, but the clunkiness usually isn’t the prompts, it’s the interaction pattern.

A few ideas that complement (and sometimes disagree with) their approach:

  1. Flip the order: human first, AI second (controversial but works)
    Instead of “AI reviews, then human validates,” try:

    • Dev opens PR and writes a short self-review in the description: what changed, known tradeoffs, what they’re unsure about.
    • AI is prompted with that self-review and the diff to:
      • Challenge assumptions
      • Check parts the author is specifically unsure about
        This kills a ton of noise. The AI is no longer trying to be a universal critic, it’s “focused skepticism.”
        It also trains devs to think more intentionally before throwing code at the bot.

  2. Inline comments over bulk summaries (for day-to-day use)
    I slightly disagree with having only a single top-level comment as the main UI.
    For many teams, it’s actually smoother if the AI leaves minimal inline comments on the diff, same as a human:

    • One comment per real issue
    • Hard cap per PR (like 5)
    • Plus a single summary comment that links to a “full AI report” for nerds who want it
      This mirrors how reviewers already work, which lowers the mental overhead. No one wants to click away to another panel for basic stuff.

  3. Use “scopes” instead of “phases” in the UI
    The phase A / B stuff is good for prompts, but in the UI it can feel like too many steps.
    I’d expose it like toggleable scopes:

    • Bugs & correctness
    • Security
    • Style & readability
    • Design & architecture
      Then pass that as metadata into the AI prompt. So if the user only cares about bugs, they don’t even see the style nags. That alone makes things feel way less clunky.

  4. Turn AI findings into actions, not text
    Instead of just “Accept / Reject”, let “Accept” optionally mean:

    • Apply suggested patch directly (for trivial fixes)
    • Open a ticket in your tracker
    • Convert to a draft PR comment
      This keeps the loop tight: see issue → click once → it becomes part of actual work.
      If your current flow is “copy text from AI → paste in Jira / PR / whatever,” that’s the clunk right there.

  5. Make the AI respect context & existing patterns
    Biggest source of useless comments: AI ignoring repo conventions.
    Add repo-level context to the prompt:

    • “These are our style rules / architecture decisions / known tech debt areas”
    • “Avoid suggesting pattern X, we deliberately don’t use it”
      Store that as a project profile and reuse across reviews.
      This cuts down on “please convert everything to dependency injection factories” every other PR.

  6. Add a “why this is useful/useless” quick-tag in the human review
    When users reject a finding, don’t just log a reason text. Give them tiny canned tags:

    • “False positive”
    • “Trivial / not worth changing”
    • “Conflicts with team convention”
    • “Already handled elsewhere”
      These tags are gold if you later want to:
    • Adjust prompts
    • Change thresholds
    • Or build per-repo rules like “stop suggesting X in this project”

  7. Use risk-based triggering instead of only size-based
    I partially disagree with only using LOC or labels. LOC is a blunt instrument.
    Consider also:

    • Files touched include security-sensitive areas
    • Changes touch auth, payments, data access, infra configs
    • High cyclomatic complexity functions modified
      AI auto-runs on those, even if small. For boring feature tweaks, make it opt-in.

  8. Give devs a “defensive mode” for messy WIP PRs
    Early WIP PRs + AI review = annoying spam.
    Add a PR label or keyword like “WIP” that:

    • Disables AI review completely, or
    • Switches it to a super minimal “only obvious bugs / security” mode
      That keeps your devs from feeling like they’re being nagged while still sketching.

  9. Move some of the AI load before the PR
    Instead of everything happening at PR time, add:

    • Editor / pre-commit command: “AI quick scan this file for obvious bugs & missing tests”
      That way, by the time the PR is up, the AI review is more like a second pass, not a big surprise dump.

  10. Measure if this is actually helping
    To know if your structure is “best” or just “different clunky,” track:

  • Median time spent on AI review per PR
  • Percentage of AI findings accepted vs rejected
  • Time to first human review with vs without AI
  • Did bug escapes go down in areas the AI comments on?
    If you see a high reject rate on a certain category, kill that category or tighten the rules. Don’t be sentimental about it.

If you describe your current stack (GitHub / GitLab / Bitbucket / custom), team size, and whether you have strong linting/CI already, you can probably cut your workflow down to something like:

  • Dev writes PR with a 3–5 line self-review
  • AI runs with scopes chosen by author
  • AI leaves at most N inline comments + 1 summary
  • Author bulk triages via quick buttons, with patches where safe
  • Human reviewer sees a cleaned-up view: “AI issues accepted / rejected / ignored”

That usually feels a lot less clunky in practice than a big “AI review screen” that everyone has to treat as a separate ceremony.

4

You’re not just fighting prompts, you’re fighting orchestration. Let me zoom in on the parts that usually make this feel clunky and how to smooth them out, riffing a bit off what @cazadordeestrellas already said and occasionally disagreeing.

1. Decide what the AI is on your team

Right now you probably have a blurry role: “AI reviews code.” That’s vague and guarantees awkwardness.

Give it a defined persona per repo:

  • “Static-analysis++ bot”
    • Focus: correctness, security, missing tests
    • No taste opinions, no architecture sermons
  • “Junior teammate”
    • Focus: questions, clarifications, alternatives
    • Less hard judgments, more “did you consider X?”
  • “Checklist executor”
    • Runs a fixed checklist for regulated / risky areas

Pick one per project. Mixing all three in a single run is where most clunk comes from, even if your prompts are great. I slightly disagree with always aiming for “junior dev + checklist”; in a high-automation environment, a ruthless static-analysis persona with minimal commentary can be far less noisy.

2. Invert ownership, not just order

Instead of “AI reviews, humans respond,” try “humans pull from the AI when needed.”

Concrete pattern:

  • PR template has:
    • Run AI for correctness
    • Run AI for security
    • Run AI for style
  • Each checkbox triggers a different AI job / comment set.

So the author opts into the categories they want. That flips the dynamic: the AI is a tool drawer, not an uninvited reviewer. It also makes post‑hoc triage less painful because devs already scoped the review.

3. Collapse your validation step

A lot of clunkiness comes from treating “user validates AI suggestions” as its own ceremony.

Better: validation = normal PR interactions.

Pattern:

  • AI leaves comments or suggestions.
  • Instead of a separate validation UI, reuse existing actions:
    • “Resolve” = rejected or already handled.
    • “Apply suggestion” or new commit = accepted.
    • “Reply with explanation” = accepted-but-modified.

You can still log telemetry behind the scenes, but from the developer’s POV, they are just doing normal PR review. No extra screen, no extra workflow. I disagree with adding many new buttons or tags visibly; they’re great for analytics, terrible for perceived friction if you surface them aggressively.

4. Teach the AI about review stages

Right now you may have a single “review” mode. Instead, use stage-aware behavior:

  1. Pre‑PR (local or pre‑commit)

    • Super fast, small model
    • Only obvious bugs & type issues
    • No architecture, no style

  2. Early PR / WIP

    • Limited to:
      • Red flags (security, data loss)
      • “You forgot to update X that is usually paired with this file”
    • Zero nitpicks

  3. Final review

    • Full scope as configured (correctness, tests, style)
    • This is where you allow deeper suggestions

The trick: stage is derived automatically from PR metadata (WIP label, draft status, target branch). You avoid manually “phasing” the review while still getting different behaviors.

5. Make the AI review narrow but repeatable

Instead of one big monolithic review, split into independent check types:

  • “Dependency / config risk scan”
  • “Public API changes & backward compatibility”
  • “Test coverage risk check”
  • “Data & privacy handling”
  • “Concurrency / performance hot spots”

Then:

  • Each check is a separate, small AI call.
  • They can run in parallel.
  • You can enable/disable them per repo or per directory.

That way, if your team hates “style” checks, you literally turn that one off, not try to fight the prompt. This is also where a named product like AI code review shines in docs or README: you can describe each check as a feature and let teams toggle them.

Pros of this style (AI code review style modular checks):

  • Very configurable per project or team
  • Easier to debug prompt quality, since each check is focused
  • Lets you iterate incrementally instead of redesigning the whole flow

Cons:

  • More moving parts to orchestrate in CI / PR hooks
  • Requires someone to “own” the library of checks
  • May feel fragmented if your UI doesn’t present all results cohesively

6. Push the AI to ask questions in tricky areas

Where I differ a bit from @cazadordeestrellas: I think for complex domains (payments, auth) you actually want the AI to ask questions instead of only asserting.

Example inline comments:

  • “This function changes payment routing logic. Is there an existing rollback path for failed routing decisions?”
  • “You handle JWT expiry here but not clock skew. Is that deliberate?”

Then give the author quick reply buttons like:

  • “Intended. Added explanation in code comments.”
  • “Good catch. Will fix.”
  • “Already handled in file X.”

This blends review with documentation, and the “user validation” step becomes answering targeted questions, not just approving / rejecting suggestions.

7. Focus on diff‑aware prompts instead of whole‑file sermons

If you feed the entire file and ask for a generic review, you get essays and a clunky triage step.

Tune the workflow to be hyper diff‑focused:

  • Primary prompt context:
    • The diff
    • A very short summary of how the file is usually used
  • Explicit instructions:
    • “Do not comment on code that is shown as unchanged.”
    • “If a problem requires touching large unchanged areas, summarize once at top level.”

This reduces AI output size and makes human validation way cheaper cognitively.

8. Use “confidence tiers” to drive what humans see

Instead of a flat list of findings:

  • Tier 1: High confidence, low disruption
    • Syntax & clear API misuse
    • Safe refactors with small patches
    • Show these by default, suggest auto‑apply
  • Tier 2: Medium confidence, medium disruption
    • Possible logic bugs, suspicious conditionals
    • Show inline, but no auto‑apply
  • Tier 3: Low confidence, big structural changes
    • Architecture, large refactors
    • Hide behind “Show advanced AI suggestions”

Your validation flow can be tier aware:

  • Tier 1:
    • One‑click apply or ignore
  • Tier 2:
    • Requires author to leave a short note if they ignore (optional but encouraged)
  • Tier 3:
    • Only visible if a human explicitly opens the “advanced” section

This keeps the workflow light for most PRs and still allows power users to dig deeper.

9. Don’t try to be smarter than your existing tooling

If you already have strong linters, type checkers and security scanners, the AI should not compete with them.

Use it for:

  • Cross‑file reasoning
  • “This test change does not actually exercise the new branch you added”
  • “This feature adds a new error state that is not surfaced in the UI layer”

And explicitly tell the AI:

  • “Assume static analysis and linting are correct and already running. Do not restate their findings. Focus on relationships between files, behavior, and tests.”

That alone removes a lot of duplicate noise that makes the AI review feel clunky compared to your trusted tools.

10. How to know if your workflow design is “good enough”

You do not need perfect. You need “non‑annoying, net useful.”

Watch these signals over a few weeks:

  • Are devs voluntarily re‑triggering AI checks on their PRs?
  • Do human reviewers say “the AI already caught the obvious stuff, I can focus on deeper review”?
  • Did the number of real bugs caught during AI vs during human review change?
  • Are conversations moving from “AI is wrong again” to “AI surfaced this question, here’s the rationale”?

If the answer to the first two is “yes,” your structure is working, even if the prompts are still evolving.

If you share your stack (GitHub / GitLab / internal), how you trigger the bot today, and where the “clunk” is most painful (too much text, wrong timing, irrelevant comments), it’s possible to sketch a very concrete interaction pattern that bolts onto what you already have rather than forcing a shiny but heavy new flow.