Build with AI: Developing a Code Review Assistant

1h 35mAdvanced2025-08-21

Authors

Pragmatic AI Labs

Alfredo Deza

Course details

Explore the intricacies of Python programming with an emphasis on mastering data structures and gain a competitive edge by learning to write cleaner, more efficient, and highly organized code. This course helps you analyze and manipulate data effectively using Python’s powerful features, and is ideal for software developers, data scientists, and tech enthusiasts eager to refine their coding skills and elevate their programming knowledge.

Learn about key concepts such as lists, dictionaries, and sets, while understanding how they can enhance your code efficiency. Delve into the optimization of algorithms to improve your problem-solving capabilities. Uncover best practices for organizing and managing data in complex systems. Check out this course to learn how to confidently tackle coding challenges with improved speed and precision, making you a valuable asset in any development team.

Skills covered

Software Quality AssuranceTelecommunicationsProgramming FoundationsFull-Stack Web DevelopmentArtificial Intelligence FoundationsArtificial Intelligence (AI)Web DevelopmentNetwork and System AdministrationSoftware DevelopmentOne-Off

Concepts

1. Foundations and Architecture

01 - Course introduction and learning objectives
02 - Automated code review - Reducing latency and cognitive load
03 - System architecture - Event-driven patterns for PR analysis
04 - GitHub Actions - Workflow orchestration and YAML configuration
05 - LLM integration - Token optimization and context window management
06 - Module summary and key architectural patterns

2. Strategy and Tooling

07 - Strategic approach to automated review systems
08 - PMAT architecture - Parser design and AST analysis
09 - Codifying review standards - Linting rules and semantic analysis
10 - Comparative analysis - Existing GitHub Action implementations
11 - Prompt engineering - Temperature control and response determinism
12 - Module summary and tool selection criteria

3. Implementation

13 - Implementation strategy and development workflow
14 - Documentation-driven development - API contract definition
15 - Building the action - TypeScript implementation and Docker packaging
16 - Test harness development - Mocking GitHub API responses
17 - Local testing - Act runner and environment simulation
18 - Module summary and performance benchmarks

4. Deployment and Production

19 - Production deployment considerations
20 - GitHub Action registration - Permissions and security boundaries
21 - PR integration - Webhook handling and comment threading
22 - Production challenges - Hallucination mitigation and rate limiting
23 - Advanced features - Incremental diff analysis and caching strategies
24 - Module summary and production metrics

5. Publishing and Distribution

25 - Distribution strategy and marketplace requirements
26 - Technical documentation - Action metadata and usage examples
27 - Marketplace publication - Versioning and semantic release
28 - Course conclusion and future enhancements