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

Instructor: Alfredo Deza

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

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

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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
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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 videos7 readings1 assignment

18 videosTotal 71 minutes

Introduction2 minutes
Why Automate Code Reviews with AI5 minutes
Architectural Overview4 minutes
Understanding GitHub Actions Basics5 minutes
Leveraging LLMs for Development5 minutes
Conclusion1 minute
Introduction1 minute
Overview of the PMAT Tool3 minutes
Defining Review Criteria and Standards6 minutes
Overview of a Similar GitHub Action4 minutes
Initial Prompt Strategy5 minutes
Conclusion1 minute
Introduction1 minute
Defining Your Strategy with Documentation6 minutes
Creating the GitHub Action6 minutes
Adding Tests and Refining5 minutes
Local Testing Strategies9 minutes
Conclusion1 minute

7 readingsTotal 70 minutes

About This Course10 minutes
Key Terms10 minutes
Reflection10 minutes
Key Terms10 minutes
Reflection10 minutes
Key Terms10 minutes
Reflection10 minutes

1 assignmentTotal 5 minutes

Building an AI Code Review System5 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 videosTotal 32 minutes

Introduction2 minutes
Creating the GitHub Action in GitHub4 minutes
Using the Bot in a Pull Request4 minutes
Challenges with Generative AI4 minutes
Exploring Additional Features4 minutes
Conclusion1 minute
Introduction1 minute
Writing Clear Action Documentation5 minutes
Publishing to GitHub Marketplace4 minutes
Conclusion2 minutes

4 readingsTotal 40 minutes

Key Terms10 minutes
Reflection10 minutes
Key Terms10 minutes
Reflection10 minutes

1 assignmentTotal 5 minutes

Deploying and Publishing AI Code Review5 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

1 reading1 assignment

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Alfredo Deza

Pragmatic AI Labs

19 Courses633 learners

Offered by

Pragmatic AI Labs

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