When Should You Trigger AI Code Review: PR Creation vs Every Push?
The Problem
When I set up AI code review in my CI/CD pipeline, I faced a choice that seemed simple but had expensive consequences:
Should I trigger review on PR creation? Every push? Both?I tried “every push” first. After one week, my API costs had tripled. My team complained about notification fatigue from duplicate review comments. And we weren’t getting better reviews - just more of them.
This post explains what I learned about trigger timing, and how to choose the right strategy for your team.
What I Tried First (And Why It Failed)
My initial GitHub Actions workflow looked like this:
name: AI Code Review
on: pull_request: types: [opened, synchronize] # This was my mistake
permissions: contents: read pull-requests: write
jobs: ai-review: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - uses: anthropics/claude-code-action@v1 with: claude_api_key: ${{ secrets.ANTHROPIC_API_KEY }} prompt: "Review this PR for bugs, security issues, and code quality."The synchronize event triggers on every push to an open PR. My developers typically push 3-5 times per PR before merge. Each push triggered a full review.
The math hit hard:
Our typical PR pattern:- 100 PRs per month- Average 4 pushes per PR (initial + 3 updates)- $0.15 per 1K tokens, ~10K tokens per review
Monthly cost:- PR creation only: 100 reviews = $150- Every push: 400 reviews = $600Four times the cost for reviews that mostly repeated themselves.
What the Community Recommends
I checked Reddit to see how other teams handle this. The consensus was clear:
“My company does use it and we do it on PR creation” - ryan_the_dev
“This is just like… the normal time to run any form of automated CI or review process.” - Connguy
The industry standard is PR creation, not every push. But there are edge cases where different strategies make sense.
Trigger Strategy Comparison
Here’s what I found after testing all four approaches:
┌─────────────────────┬────────┬────────────────┬─────────────┬─────────────────────┐│ Trigger Type │ Cost │ Review Quality │ Noise Level │ Best For │├─────────────────────┼────────┼────────────────┼─────────────┼─────────────────────┤│ PR Creation Only │ 1x │ High │ Low │ Most teams (default) ││ Every Push │ 3-5x │ Variable │ High │ Security-critical ││ Manual Trigger │ Lowest │ High │ None │ Budget-conscious ││ Hybrid (Smart Push) │ 1.5-2x │ High │ Medium │ Large PRs │└─────────────────────┴────────┴────────────────┴─────────────┴─────────────────────┘Let me explain each strategy in detail.
Strategy 1: PR Creation Only (Recommended)
This is what most teams should use. The workflow triggers once when a PR opens:
name: AI Code Review
on: pull_request: types: [opened]
permissions: contents: read pull-requests: write
jobs: ai-review: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - uses: anthropics/claude-code-action@v1 with: claude_api_key: ${{ secrets.ANTHROPIC_API_KEY }} prompt: | Review this PR for: - Logic errors and bugs - Security vulnerabilities - Code style and best practices - Missing tests
Provide actionable feedback in a constructive tone.Why this works:
- One review per PR, predictable costs
- Full diff context for the reviewer
- Aligns with human review timing (reviewers check PRs once, not on every push)
- No notification spam
Cost estimate for 100 PRs/month: ~$150
Strategy 2: Every Push to PR
This triggers on both opened and synchronize:
name: AI Code Review (Every Push)
on: pull_request: types: [opened, synchronize]
permissions: contents: read pull-requests: write
jobs: ai-review: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - uses: anthropics/claude-code-action@v1 with: claude_api_key: ${{ secrets.ANTHROPIC_API_KEY }} prompt: | Review the latest changes in this PR. Focus on incremental changes since the last review. Update existing comments rather than creating duplicates.When this makes sense:
- Security-critical repositories (catch issues before they compound)
- Teams with many junior developers (early feedback helps them learn)
- Compliance-heavy environments where every change needs review
The downside:
- 3-5x higher costs
- Notification fatigue from repeated reviews
- Duplicate comments unless you configure smart updates
- Reviews become less valuable as noise increases
Cost estimate for 100 PRs/month (avg 4 pushes/PR): ~$600
Strategy 3: Hybrid Approach (PR Creation + Smart Incremental)
This is the sophisticated option. Full review on PR creation, then targeted updates on subsequent pushes:
name: AI Code Review (Hybrid)
on: pull_request: types: [opened, synchronize]
permissions: contents: read pull-requests: write
jobs: ai-review: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0
- name: Determine review type id: review-type run: | if [ "${{ github.event.action }}" == "opened" ]; then echo "type=initial" >> $GITHUB_OUTPUT else echo "type=update" >> $GITHUB_OUTPUT fi
- name: Initial PR Review if: steps.review-type.outputs.type == 'initial' uses: anthropics/claude-code-action@v1 with: claude_api_key: ${{ secrets.ANTHROPIC_API_KEY }} prompt: | Perform a comprehensive review of this new PR. Create a summary comment with key findings. Prioritize: security > bugs > style > suggestions.
- name: Incremental Review if: steps.review-type.outputs.type == 'update' uses: anthropics/claude-code-action@v1 with: claude_api_key: ${{ secrets.ANTHROPIC_API_KEY }} prompt: | Review only the new changes pushed to this PR. Update the existing review comment, do not create new ones. Stop early if previous concerns have been addressed. Focus on whether the push fixed flagged issues.The key difference: incremental reviews update existing comments instead of creating new ones, and they stop early if concerns are resolved.
When this makes sense:
- Large teams with complex PRs
- Critical codepaths where every change matters
- Teams that want early feedback without full review noise
Cost estimate for 100 PRs/month: ~$225 (1.5x baseline)
Strategy 4: Manual Trigger Only
Maximum control, minimum surprise:
name: AI Code Review (Manual)
on: workflow_dispatch: inputs: pr_number: description: 'PR number to review' required: true type: number review_depth: description: 'Review depth' required: true default: 'standard' type: choice options: - quick - standard - thorough
permissions: contents: read pull-requests: write
jobs: ai-review: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - uses: anthropics/claude-code-action@v1 with: claude_api_key: ${{ secrets.ANTHROPIC_API_KEY }} prompt: | Review PR #${{ inputs.pr_number }} with ${{ inputs.review_depth }} depth. Focus on actionable, high-priority feedback.When this makes sense:
- Teams testing AI review before full rollout
- Budget-conscious organizations
- Projects where AI review is optional, not mandatory
Cost: On-demand, fully controllable
How I Decided
After testing all four approaches, here’s the decision framework I use:
┌──────────────────────┐ │ Do you need AI review │ │ on every commit? │ └──────────┬───────────┘ │ ┌───────────────┼───────────────┐ ▼ │ ▼ YES NO MAYBE │ │ │ ▼ ▼ ▼ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ Every Push │ │ PR Creation │ │ Hybrid │ │ or Hybrid │ │ Only │ │ Approach │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ ▼ ▼ Security-critical? Large PRs? Junior-heavy team? Critical paths?For my team, PR creation only works best. We’re experienced developers working on a typical SaaS product. We don’t need hand-holding on every push.
Common Mistakes I Made
Mistake 1: Defaulting to “more review is better”
I assumed more AI reviews would catch more issues. In reality, developers started ignoring notifications. Noise reduced the value of legitimate findings.
Mistake 2: Not considering the PR lifecycle
I didn’t account for how developers work. They push, get CI feedback, fix, push again. The “fix typo” push doesn’t need a fresh review.
Mistake 3: Copying other teams’ configurations blindly
I saw a blog post recommending synchronize triggers and copied it. That team had different constraints: security-critical code, unlimited budget, and a compliance requirement for review logging.
Practical Recommendations
Start with PR creation only. Monitor for 2-4 weeks. Then ask:
- Are we catching the issues we care about?
- Is notification noise a problem?
- Are costs predictable?
If answers are yes, stick with it. If not, consider:
- Hybrid approach if you need more coverage without full “every push” costs
- Manual trigger if budget is tight or AI review is experimental
- Every push only if you have a specific security or compliance requirement
Summary
AI code review trigger timing affects both cost and review quality. Most teams should start with PR creation only, which provides:
- Predictable costs (1x per PR)
- Full review context
- Low notification noise
- Alignment with human review timing
The “every push” strategy costs 3-5x more and creates notification fatigue. Use it only for security-critical code or junior-heavy teams. The hybrid approach offers a middle ground with smart incremental updates.
My recommendation: start simple, measure results, then iterate. The best configuration is the one your team actually uses without tuning it out.
Final Words + More Resources
My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me
Here are also the most important links from this article along with some further resources that will help you in this scope:
- 👨💻 Claude Code GitHub Actions
- 👨💻 GitHub Copilot Code Review
- 👨💻 Reddit: When do you trigger AI code review?
Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!
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