← Use Cases

Automated PR Review and Merge Readiness

An example layered AI review workflow that raises PR quality before human approval

Industry general
Complexity intermediate
Tools: claude code , cursor , ollama
Models: gpt 5 codex , claude opus 4 6 , gpt 5
pull-request code-review merge-readiness ci developer-productivity
Updated February 15, 2026

The Challenge

PR review quality often varies based on reviewer availability and domain familiarity. Some pull requests receive deep scrutiny while others get mostly formatting feedback. This inconsistency increases regression risk and slows teams during busy release windows.

This use case proposes a pre-review filter that can:

  • Catch correctness and security issues earlier.
  • Generate actionable findings instead of generic suggestions.
  • Keep human reviewers focused on architecture and product impact.

Suggested Workflow

Use a layered “AI review before human review” flow:

  1. Self-check pass (GPT-5 Codex): the author reviews their own diff with a strict rubric.
  2. Risk pass (Claude Opus): an independent model checks regressions, edge cases, and test gaps.
  3. Policy pass (local via Ollama): enforce repository-specific rules when code should stay local.
  4. Human pass: reviewers receive pre-ranked findings and focus on final judgment.

This structure can reduce low-value back-and-forth and improve signal density in review comments.

Implementation Blueprint

Trigger this sequence for PRs above a minimum size threshold:

Input: git diff + affected tests + related ticket
Output:
1) severity-ranked findings
2) missing test recommendations
3) merge-readiness score
4) required follow-ups before approval

Review prompt:

Review this diff as a strict senior engineer.
Prioritize findings by severity (critical/warning/suggestion).
Check:
- correctness and edge cases
- security and unsafe data handling
- performance regressions
- test coverage gaps
Provide concrete code-level fixes.
Conclude with merge readiness: ready / needs changes.

Practical details:

  • Post findings as PR comments with concise fix guidance.
  • Track repeated false positives and tune prompt rules.
  • Gate merge on either “ready” or all critical findings resolved.
  • Allow explicit lead override for urgent hotfixes.

Potential Results & Impact

Teams that implement this flow can expect:

  • Higher first-review defect catch rate.
  • Faster review turnaround because PRs arrive better prepared.
  • More reviewer time spent on architecture, less on obvious issues.
  • Better feedback consistency for junior engineers.

Useful metrics: first-review defect catch rate, time-to-approval, number of reopened PRs, and post-merge incident rate.

Risks & Guardrails

Risks:

  • Running all checks on tiny diffs adds noise and latency.
  • Long context dumps dilute model focus.
  • Teams treat AI verdicts as approval authority.

Guardrails:

  • Apply size- or risk-based trigger rules.
  • Cap context per run and link additional files only as needed.
  • Keep findings concise: issue, impact, fix.
  • Reserve final merge approval for humans.

AI review works best as a quality accelerator, not as a replacement for engineering judgment.

Tools & Models Referenced

  • Claude Code (claude-code): Automates structured review workflows across files.
  • Cursor (cursor): In-editor review loop and fast remediation edits.
  • Ollama (ollama): Local policy checks for sensitive repositories.
  • GPT-5 Codex (gpt-5-codex): Strong diff analysis and concrete code-level fixes.
  • Claude Opus 4.6 (claude-opus-4-6): Robust second-pass reasoning for hidden risk.
  • GPT-5 (gpt-5): PR summarization and merge-readiness handoff notes.