AR Academy Library · Lesson 3 of 5 · $4,999 for all 5, waived on scoping call
EnrollPR pipeline with AI reviewer stage
What AI reviewers catch vs. what humans catch
AI reviewers are good at what humans do poorly at 4pm on a Friday: consistency checks, obvious null-safety issues, forgotten test cases, docs drift, and matching PR content against the description. They are not good at architectural judgment, cross-file security reasoning, or knowing what your team decided in a Slack thread last week. Treat them as a tireless junior — helpful for catching the boring stuff, not for approving the shape of a system.
The reviewer pipeline — where AI fits in
The pipeline is: PR opens, static analysis and linters run first (fast, deterministic, cheap), then the AI reviewer runs (slower, probabilistic, expensive), then a human reviews the AI's output alongside the diff. Do not put the AI first — you will waste tokens on issues the linter already caught. Do not skip the human — the AI will confidently suggest wrong things. The AI's job is to make the human review faster, not to replace it.
Prompt design for review quality
The prompt that ships: system role explaining your codebase's conventions, retrieved context for the touched files (not the whole repo), the diff, the PR description, and the CI results. Ask for specific outputs — bugs found, missing tests, doc drift, style concerns — not "review this PR." Score confidence per comment. Reject low-confidence noise before it hits the PR. Every generic "consider adding error handling" comment trains reviewers to ignore the tool.
Cost and latency budgets for review agents
A review agent that costs $2 per PR and takes 90 seconds is invisible on 100 PRs a week. At 10,000 PRs a week, it's a $10k/month line item and a bottleneck. Cache retrieved context aggressively, route small diffs to Haiku and big ones to Sonnet, and cap per-PR spend. Latency budget: reviewer runs in parallel with tests, not sequentially. If it can't finish before humans start looking, it's not adding leverage.
Metrics that show whether it's working
The metric that matters is not "comments per PR." It's whether reviewer-caught issues are the ones humans would have missed. Track: reviewer comment resolution rate (accepted/dismissed), time-to-first-human-comment (does AI reduce it?), regression rate on merged PRs (does AI catch what CI misses?), and cost per merged PR. When resolution rate drops below 30%, the reviewer is noisy — tighten the prompt or lower the confidence threshold.
Enroll — 30 min scoping call.
We build what these lessons describe. $4,999 library, waived when we scope work.
