LLM-Based Code Evaluator

Built an automated code evaluation system using LLMs to assess code quality, correctness, and adherence to requirements — replacing slow manual review workflows.

Key Highlights:

• 3× throughput improvement over manual evaluation baseline
• 90% reduction in manual review time
• Multi-threaded Streamlit UI enabling parallel evaluation of multiple submissions

Technical Approach:

• OpenAI API for LLM-based code assessment with structured prompt engineering
• Multi-threaded execution for parallel evaluation runs
• Configurable scoring rubrics per evaluation criteria (correctness, style, efficiency)
• Streamlit frontend for reviewers to inspect and override evaluations

Tech Stack: Python OpenAI API Streamlit Prompt Engineering Multi-threading

Impact: Scaled code review capacity by 3× while cutting reviewer time by 90%, enabling faster hiring and assessment pipelines.