AI-Assisted Frontend Reviews Using Gemma 4

This is a submission for the Gemma 4 Challenge: Build with Gemma 4

PR Sentinel analyzes React and TypeScript snippets and generates structured engineering feedback focused on maintainability, accessibility, performance, and UI quality.

What I Built

PR Sentinel is an AI-assisted frontend PR reviewer focused on React and TypeScript engineering quality.

Developers can paste frontend code snippets and receive structured engineering feedback across:

• React best practices


• Maintainability


• UI/UX quality


• Accessibility (A11y)


• Component architecture


• State management patterns

The project was inspired by real frontend review pain points commonly seen in enterprise applications, especially issues related to stale closures, infinite re-renders, semantic accessibility structure, and reusable component design.

Instead of producing generic AI summaries, PR Sentinel organizes feedback into categorized engineering review cards that resemble actual senior-level frontend review comments.

Demo

Live Demo

Live demo uses a limited development API configuration and may occasionally be unavailable during evaluation periods.

Code

(https://github.com/naomirasamalla/Frontend-PR-Review-Assistant)

The application allows developers to paste frontend snippets and receive categorized AI-generated engineering review feedback in real time.

Key Features:

AI-powered React/TypeScript review analysis

Structured frontend engineering feedback

Accessibility-focused review insights

UI/UX and maintainability recommendations

Real-time review rendering

How I Used Gemma 4

PR Sentinel uses Gemma 4 to analyze React and TypeScript frontend code snippets and generate structured PR-style engineering feedback.

The project focuses on identifying practical frontend issues such as:

• infinite render loops


• stale closures in async logic


• unsafe DOM access patterns


• maintainability concerns


• accessibility-related frontend risks

Gemma 4 was selected because the project required fast reasoning over frontend engineering patterns while generating concise, developer-focused review output.

The model is used to evaluate pasted code snippets and return structured recommendations similar to a lightweight frontend pull request review workflow.

The application includes built-in diagnostic sandbox scenarios that simulate real frontend engineering issues commonly encountered in React applications.