In early 2025, a tweet by Ted Werbel went viral among developers for its practical approach to debugging with AI tools. Shared by @pyquantnews on May 2, 2025, the advice has since reshaped how many interact with AI coding assistants.
A Systematic Approach to AI Debugging
The core idea is simple yet powerful: break down a coding problem systematically before diving into fixes. Here’s the prompt that started it all:
"Reflect on 5–7 different possible sources of the problem, distill those down to 1–2 most likely sources, and then add logs to validate your assumptions before we move onto implementing the actual code fix."
This method ensures AI tools don’t jump to premature conclusions, a common issue in AI-assisted coding. In 2025, a Stack Overflow survey noted a 30% rise in AI-assisted coding adoption, highlighting the need for such structured prompts.
How to Apply This in Your Workflow
Start by identifying 5–7 potential causes of your coding issue. Narrow these to 1–2 most likely culprits. Then, use logging to confirm your suspicions before asking the AI to suggest a fix. This reduces errors and improves AI output accuracy.
For more on crafting effective AI prompts, check out this guide from Kipwise on best practices for AI interactions.