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Case Study

AI Code Reviewer

An AI-powered developer assistant that analyzes source code and returns quality score, actionable suggestions, easy explanations, and improved code fixes using Groq LLM.

Problem

Developers spend significant time in manual code reviews. Feedback can be inconsistent, and beginners often struggle to understand why code quality is low.

Solution

This project provides instant AI feedback through a clean coding interface. Users can paste or upload code and receive analysis, score, explanation, and improved code in one workflow.

Key Features

Frontend Stack

Next.jsTypeScriptTailwind CSSshadcn/uiMonaco Editor

Backend Stack

Node.jsExpressGroq SDK (llama3-70b-8192)

Architecture Flow

  1. User enters or uploads source code in Monaco Editor.
  2. Frontend sends code and selected language to backend endpoints.
  3. Backend calls Groq LLM for analysis and explanation generation.
  4. Response returns score, suggestions, explanation, and fixed code.
  5. User reviews output and can apply AI-suggested fix instantly.

API Endpoints

POST /api/analyze-code: returns score, suggestions, and fixed code.

POST /api/explain-code: returns plain-English explanation of code.

{
  "code": "function sum(a, b) { return a + b; }",
  "language": "javascript"
}

Run Locally

Backend:

cd backend
npm install
npm run start

Frontend:

cd frontend
npm install
npm run dev

Future Improvements

This is one of my practice projects, built to strengthen real-world full-stack and AI integration skills.