Field notes on AI code review worth reading
Opinionated engineering notes on the hard part of AI review: reducing noise, keeping humans in control, and turning model output into one useful GitHub review. For shipped changes, see the changelog.
Recent posts
A practical playbook for GitHub code review automation
Design a GitHub code review automation workflow that reacts to the right events, handles updates safely, posts readable feedback, and stays out of the merge path until it earns trust.
How to reduce false positives in AI code review
False positives are an attention problem, not just a model problem. Use scope, evidence, confidence, deduplication, caps, and dismissal data to make AI review quieter and more trustworthy.
BYOK and OpenRouter for AI code review: an engineering guide
Understand what bring-your-own-key changes in an AI code review system, how OpenRouter model routing fits, and which security, cost, and reliability questions teams should answer first.
How to control AI code review costs without losing useful coverage
A cost model for automated pull-request review, with practical controls for triggers, context, model routing, reruns, large diffs, and BYOK provider spend.
How to use AI for security review on pull requests
A technically grounded workflow for AI-assisted security review: map trust boundaries, demand exploit paths, prioritize changed behavior, and keep deterministic security controls in place.
Multi-agent vs single-model AI code review
Compare one general reviewer with a panel of specialist agents, including where specialization improves coverage, where it adds cost and duplication, and how aggregation keeps the result usable.
Reviewing AI-generated code: a pull-request checklist that scales
AI-generated code needs the same standards as human code, with extra attention to intent, invented APIs, shallow tests, dependency choices, and maintainability across the full change.
AI code review for solo developers: a disciplined workflow
Use pull requests, automated checks, and an advisory AI reviewer to create a second-pass workflow even when you are the only maintainer—without pretending the bot is a teammate.
How to roll out AI code review to an engineering team
A low-risk rollout plan for automated pull-request review: establish goals, pilot in advisory mode, collect resolution data, tune repository policy, and expand only when maintainers see value.
How to measure AI code review quality
Move beyond comment counts with a measurement system for accepted and dismissed findings, precision, coverage samples, severity, maintainer effort, latency, reliability, and cost.
AI code review for monorepos: scope, context, and ownership
Make automated review useful in a monorepo with path-aware policy, dependency context, package ownership, generated-file rules, model budgets, and cross-boundary analysis.
How to review large pull requests with AI without pretending they are small
Large diffs need explicit scope, staged context, file grouping, partial-review labels, and human decomposition—not silent truncation or an oversized prompt.
How to choose a model for AI code review
Select code-review models with repository-specific tests for reasoning, precision, structured output, context use, latency, cost, privacy, and confidence calibration—not a generic leaderboard.
Designing repository YAML policy for AI code review
A practical guide to versioned AI review configuration: reviewer roles, path scopes, confidence floors, comment caps, model routing, prompt policy, validation, and safe defaults.
Human-in-the-loop AI code review: where the human should decide
Design advisory AI review so models gather evidence and prioritize findings while humans retain ownership of intent, risk, architecture, exceptions, and the final merge decision.
Incremental AI pull-request reviews without duplicate comments
Review new commits against the previous head, preserve finding identity, detect stale evidence, and update GitHub feedback without repeating the entire first review after every push.
How to write custom AI code reviewer prompts that stay useful
Turn repository knowledge into focused reviewer prompts with explicit scope, evidence requirements, exclusions, severity, confidence, output schemas, and versioned tests.
Acceptance and dismissal metrics for AI code review
Build a useful feedback loop from finding resolutions without confusing accepted comments with truth, dismissals with failure, or easily gamed rates with engineering quality.
AI code review vs CI and static analysis: what should run where?
Use deterministic tools for repeatable enforcement and AI review for contextual reasoning. This guide maps types, tests, linters, scanners, policy checks, and advisory model feedback into one workflow.
Why three specialist reviewers beat one generic bot
A single “review this PR” prompt gets distracted. PR Quorum splits review into Correctness, Security, and Architecture so each reviewer can stay sharp and the aggregator can keep the final comment clean.
Deduping reviewer findings without losing signal
How we sort by severity, dedupe by (file, line, lowercased title), and only post inline comments on lines that map to a unified-diff position. Plus: what we threw out and why.
A confidence floor is the cheapest noise filter you have
PR Quorum defaults to a 0.75 confidence floor because the fastest way to earn trust is not posting weak findings in the first place.
AI review should be advisory by default
Why every PR Quorum review is posted with event:COMMENT — never request_changes — and how the advisory framing changes how teams actually use the panel.
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