AI Code Evaluation & Testing Workflow

Overview

A structured workflow for evaluating AI-generated code based on correctness, functionality, readability, edge cases, and instruction-following quality.

Problem / Context

AI-generated code needed consistent, rigorous evaluation to be useful as training signal. Ad-hoc reviews lacked repeatability and clear criteria across correctness, readability, and edge cases.

My Role

I evaluated frontend and full-stack coding outputs, designed test cases, reviewed code quality, and wrote structured technical feedback to improve dataset reliability.

What I Built

• Evaluation rubric spanning correctness, readability, and edge cases
• Test cases to exercise expected and boundary behavior
• Structured feedback templates for consistent reviews

Tech Stack

Key Features

• Repeatable evaluation criteria across submissions
• Edge-case analysis and debugging of broken outputs
• Instruction-following assessment for prompt adherence

Challenges

• Keeping evaluations objective and consistent at scale
• Identifying subtle correctness and edge-case failures
• Communicating issues clearly through structured feedback

Outcome

• Higher-quality, more reliable evaluation datasets
• Clearer signal on code correctness and instruction-following