A practical way to evaluate AI coding assistants

A developer proposes a lightweight evaluation framework for AI coding assistants, focusing on five key questions and consistent testing across tools. The approach emphasizes correctness, review effort, and maintainability over feature lists. A public AI Coding Tools Guide is available for teams to shortlist and test assistants against their own repositories.

When a team compares AI coding assistants, the hardest part is usually not finding a longer feature list. It is deciding which tool fits the way the team actually works. A lightweight evaluation can focus on five questions: I also like to test the same small task across several assistants. Keep the prompt, repository context, acceptance criteria, and time limit consistent. Then compare the result on correctness, review effort, and maintainability—not just the first draft. A useful test set might include: For teams that want a concise starting point, I keep a public AI Coding Tools Guide https://ai-coding-tools-guide.vercel.app/ with practical notes on use cases, workflows, and developer fit. It is best used as a shortlist, followed by testing the tools against your own repository and policies. The goal is not to find the assistant with the most impressive demo. It is to find the one that reduces useful engineering work while keeping human review in the loop.