An AI-powered Python debugging platform that uses large language models (LLMs) to analyze runtime errors, explain root causes, and suggest actionable fixes. The project focuses on applying LLMs to real-world developer workflows through clean backend design and structured prompts.
- Accepts Python code and error traces as input
- Analyzes exceptions and runtime errors using LLMs
- Generates human-readable explanations and suggested fixes
- Exposes debugging functionality through a backend API
This tool is designed to reduce debugging time and improve developer productivity by augmenting traditional error messages with AI-generated insights.
This project was extended and customized to emphasize applied AI and backend system design:
- Refactored backend structure for clarity and maintainability
- Designed structured prompts for consistent and useful debugging responses
- Integrated LLM-based error analysis into an API-driven workflow
- Improved response formatting for better readability and downstream use
- Focused on production-oriented concerns such as latency, cost awareness, and reliability
- Python
- FastAPI / Flask
- OpenAI API (LLMs)
- REST APIs
- Docker (optional containerization)
This project demonstrates applied AI/ML development beyond model training, with an emphasis on:
- LLM integration into backend systems
- Prompt design and response quality
- API-based ML inference
- Building practical developer tools using AI
It reflects an end-to-end approach to AI-powered software systems, combining machine learning concepts with real-world software engineering pra