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⇱ AI Code Review Automation with GitHub Actions | Coursera

AI Code Review Automation with GitHub Actions

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AI Code Review Automation with GitHub Actions

This course is part of AI Tooling Specialization

Included with

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Build and test a custom GitHub Action that uses AI to automatically review pull requests and provide code quality feedback

  • Design prompt strategies and define review criteria using the pmat tool to produce actionable, consistent AI review output

  • Deploy your AI review bot to GitHub, use it on real pull requests, and publish it to the GitHub Marketplace

Details to know

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Recently updated!

April 2026

Assessments

3 assignments

Taught in English

Build your subject-matter expertise

This course is part of the AI Tooling Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 modules in this course

Build an AI-powered code review bot from scratch and publish it to the GitHub Marketplace. This hands-on course walks you through the complete lifecycle of creating a GitHub Action that uses Large Language Models to automatically review pull requests and provide actionable feedback on code quality.

You start by exploring why automated code review matters, examining real pull requests in complex projects, and understanding the architecture of AI review pipelines built on GitHub Actions. You then define review criteria using the pmat code quality analysis tool, study existing review actions as reference implementations, and develop prompt engineering strategies that produce useful AI feedback. In the implementation phase, you apply documentation-driven development to plan your action, build it with AI assistance, add tests, and refine through local testing strategies. You deploy the action to GitHub, use it on real pull requests, and confront practical challenges of generative AI including non-deterministic responses. The course concludes with writing clear action documentation and publishing your review bot to the GitHub Marketplace for community distribution.

Covers why automate reviews with AI, GitHub Actions architecture, PMAT complexity analysis, review criteria definition, iterative prompt strategy, action creation, testing, and local validation.

What's included

18 videos6 readings1 assignment

18 videos•Total 71 minutes
  • Introduction•2 minutes
  • Why Automate Code Reviews with AI•5 minutes
  • Architectural Overview•4 minutes
  • Understanding GitHub Actions Basics•5 minutes
  • Leveraging LLMs for Development•5 minutes
  • Conclusion•1 minute
  • Introduction•1 minute
  • Overview of the PMAT Tool•3 minutes
  • Defining Review Criteria and Standards•6 minutes
  • Overview of a Similar GitHub Action•4 minutes
  • Initial Prompt Strategy•5 minutes
  • Conclusion•1 minute
  • Introduction•1 minute
  • Defining Your Strategy with Documentation•6 minutes
  • Creating the GitHub Action•6 minutes
  • Adding Tests and Refining•5 minutes
  • Local Testing Strategies•9 minutes
  • Conclusion•1 minute
6 readings•Total 60 minutes
  • Key Terms•10 minutes
  • Reflection•10 minutes
  • Key Terms•10 minutes
  • Reflection•10 minutes
  • Key Terms•10 minutes
  • Reflection•10 minutes
1 assignment•Total 5 minutes
  • Building an AI Code Review System•5 minutes

Covers deploying the action to GitHub, using the bot in real pull requests, handling generative AI challenges (hallucination, inconsistency), writing action documentation, and publishing to GitHub Marketplace.

What's included

10 videos4 readings1 assignment

10 videos•Total 32 minutes
  • Introduction•2 minutes
  • Creating the GitHub Action in GitHub•4 minutes
  • Using the Bot in a Pull Request•4 minutes
  • Challenges with Generative AI•4 minutes
  • Exploring Additional Features•4 minutes
  • Conclusion•1 minute
  • Introduction•1 minute
  • Writing Clear Action Documentation•5 minutes
  • Publishing to GitHub Marketplace•4 minutes
  • Conclusion•2 minutes
4 readings•Total 40 minutes
  • Key Terms•10 minutes
  • Reflection•10 minutes
  • Key Terms•10 minutes
  • Reflection•10 minutes
1 assignment•Total 5 minutes
  • Deploying and Publishing AI Code Review•5 minutes

Build and deploy an AI-powered code review GitHub Action that extends [paiml/pmat-action](https://github.com/paiml/pmat-action) with LLM-based analysis, contextual PR feedback, and GitHub Marketplace publishing. The project covers the complete lifecycle from defining review criteria and prompt strategy through local testing, deployment, and documentation.

What's included

3 readings1 assignment

3 readings•Total 21 minutes
  • Capstone•10 minutes
  • Before You Go•1 minute
  • Next Steps•10 minutes
1 assignment•Total 15 minutes
  • Final Graded Quiz•15 minutes

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Instructor

Pragmatic AI Labs
35 Courses•2,961 learners

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