How to Improve the PR Review Process for Engineering Teams
Code reviews are a critical part of the software development lifecycle. They help maintain code quality, catch bugs early, share knowledge across the team, and ensure consistency in coding standards. However, if not managed well, code reviews can become a bottleneck, slow down development, and even create friction among team members.
In this blog, we'll explore practical ways to improve the code review process for engineering teams, making it more efficient, collaborative, and effective.
1. Set Clear Guidelines & Expectations
A well-defined code review process prevents ambiguity and ensures consistency.
- Define Review Criteria: What should reviewers look for? (e.g., functionality, readability, performance, security, test coverage).
- Establish SLAs for Reviews: Set expectations on response times (e.g., "All PRs should be reviewed within 24 hours").
- Document Best Practices: Maintain a checklist or style guide to standardize reviews.
2. Keep Pull Requests (PRs) Small & Focused
Large PRs are harder to review, leading to delays and overlooked issues.
- Break Down Changes: Encourage smaller, incremental PRs (e.g., 200-400 lines max).
- One PR per Feature/Bug: Avoid mixing unrelated changes.
- Use Feature Flags: If a large feature is unavoidable, use feature flags to merge safely.
3. Automate What You Can
Manual reviews should focus on logic and architecture—not formatting or syntax.
- Linters & Formatters (ESLint, Prettier, Black, RuboCop)
- Static Code Analysis (MatterAI)
- Automated Testing (Unit, Integration, E2E tests in CI/CD)
- Automated Security Scans (MatterAI)
4. Foster a Positive & Constructive Review Culture
Code reviews should be about improving code—not criticizing people.
- Be Respectful: Use phrases like "Consider refactoring this" instead of "This is wrong."
- Explain Why: Provide context for suggestions (e.g., "This could lead to a race condition because…").
- Encourage Questions: Instead of demanding changes, ask, "What was the reasoning behind this approach?"
5. Rotate Reviewers & Avoid Bottlenecks
Relying on the same few senior engineers for reviews slows down the process.
- Distribute Responsibility: Ensure multiple team members can review different areas.
- Pair Junior & Senior Engineers: Helps with mentorship and knowledge sharing.
- Use Tools Like Reviewpad or Graphite: Automate reviewer assignments based on code ownership.
6. Use Code Review Tools Effectively
The right tools streamline the process.
- GitHub/GitLab/Bitbucket PRs: Use inline comments, approval workflows, and required checks.
- Review Apps: Tools like Netlify or Heroku Preview Deploys let reviewers test changes live.
- AI-Assisted Reviews (MatterAI) can suggest improvements.
7. Track & Optimize Review Metrics
Measure what matters to continuously improve.
- Average Review Time: Are PRs sitting too long?
- Comment-to-Approval Ratio: Too many nitpicks?
- Rejection Rate: Are too many PRs being sent back?
8. Conduct Retrospectives on the Review Process
Regularly discuss what’s working and what’s not.
- Team Feedback Sessions: What slows down reviews?
- Experiment with New Approaches: Try async video reviews, live pair reviews, or mob reviews.
How can MatterAI help?
- Cut code review time by 90%
- Reduce bugs and vulnerabilities by 95%
- Reduce documentation time by 90%
- Reduce time to market by 50%
Get started today with MatterAI and experience the future of code reviews. Visit https://matterai.so to learn more or sign up for a free trial.
MatterAI builds frontier AI infrastructure for engineering teams — from inference-optimized models to autonomous coding agents and agentic code reviews.
Explore what we're building:
- Orbital IDE — Autonomous AI coding agent with background agents and deep codebase memory
- AI Code Reviews — Agentic pre-commit reviews across GitHub, GitLab, and Bitbucket
- Axon Models — Frontier-grade reasoning models at 70% lower inference cost
Share this Article:
More Articles
OrbCode: Semantic Search and Inference Optimization for Claude Code
Claude Code is powerful out of the box — but without an optimization layer, teams are silently burning tokens on bad retrieval, redundant tool calls, and unobserved inference waste. Here's how OrbCode fixes the infrastructure problem hiding inside every Claude Code workflow.
Data Annealing: The Hidden Optimization Layer Behind Modern AI Systems
Modern AI systems are no longer trained on static datasets. Frontier models continuously reshape, refine, replay, and optimize data throughout training — creating a new paradigm we call Data Annealing.
The Economics of AI Agents: How Companies Are Reducing AI Inference Costs by 70%
AI agents are becoming core infrastructure inside modern companies, but inference costs are scaling faster than most teams expect. Here's why AI agents become expensive — and how organizations are reducing operational AI costs by up to 70%.
How We Rebuilt the Context Layer Behind AI Code Review
Let's dive deep into the most advance and cost effective code reviewer
Introducing Orbital: The low cost AI Coding App Built for Engineers
A full end-to-end alternative to Cursor and Windsurf, powered by Axon LLMs with 2-5x higher usage limits and complete data privacy.
Continue Reading
OrbCode: Semantic Search and Inference Optimization for Claude Code
Claude Code is powerful out of the box — but without an optimization layer, teams are silently burning tokens on bad retrieval, redundant tool calls, and unobserved inference waste. Here's how OrbCode fixes the infrastructure problem hiding inside every Claude Code workflow.
Data Annealing: The Hidden Optimization Layer Behind Modern AI Systems
Modern AI systems are no longer trained on static datasets. Frontier models continuously reshape, refine, replay, and optimize data throughout training — creating a new paradigm we call Data Annealing.
The Economics of AI Agents: How Companies Are Reducing AI Inference Costs by 70%
AI agents are becoming core infrastructure inside modern companies, but inference costs are scaling faster than most teams expect. Here's why AI agents become expensive — and how organizations are reducing operational AI costs by up to 70%.
Ship Faster. Ship Safer.
Join thousands of engineering teams using MatterAI to autonomously build, review, and deploy code with enterprise-grade precision.