Step 01
Identify the Bug and Context
Select a specific bug, usually from a bug report or a GitHub issue. This document should clearly describe the failure and provide enough detail for the AI to understand the problem.
IntermediateEngineering
How to Fix Bugs Using an AI-Powered Test-Driven Development (TDD) Workflow
Leverage an AI coding assistant like Claude Code to implement a Test-Driven Development (TDD) process. This workflow allows you to reproduce, fix, and verify bug fixes with incredible speed and accuracy.
Try this with your AI assistant
Download the skill for this workflow. Version 1.0.1.
Source episode · 36:26How Gusto Built a New Product Line in 10 Weeks with Claude Code, No Jira, and No Docs
with Claire Vo
From 36:26 to 39:38, Eddie Kim demonstrates fixing a payroll bug in Claude Code by first creating an evaluation that reproduces the failure, then changing the code until it passes. Clip range: 36:26 to 39:38.
Before you start
What you need
- GitHub issue describing the bug
- AI coding assistant with repository access
- Existing automated test framework
- Permission to create pull requests
- Target repository branch for the fix
What you’ll make
A pull request containing a failing regression test, the implemented fix, and passing test results.
Tools used
- Claude CodeVisitClaude Code
Claude for coding tasks
The workflow
Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.
6 steps
Step 02
Craft a Detailed TDD Prompt
In your AI coding assistant, provide a prompt that points to the GitHub issue. Instruct the AI to follow a test-driven development (TDD) process: first, write a test that reproduces the bug and fails; second, propose a fix; and third, prove the fix works by showing the test now passes.
Example prompt
There's a customer issue that is outlined in this GitHub issue... Can you please read this issue and come up with a fix for the problem that's outlined here? I'd love for you to first write an eval that fails to show that you can reproduce this issue, then come up with a solution, and then prove that the solution works by showing that the eval now passes.
Step 03
AI Writes a Failing Test
The AI reads the issue and generates a test case, or evaluation, designed to fail. This failing test serves as confirmation that the AI can successfully reproduce the bug as described.
Step 04
AI Proposes and Tests the Fix
The AI then proposes a solution, which could be a code change or a modified prompt. It runs the same evaluation again, this time expecting it to pass. The passing test is the proof that the proposed fix works.
Step 05
Conduct a Human Review
Review the AI's proposed code change and the test case. Use your judgment to verify that the fix is correct, high quality, and does not introduce any unintended side effects.
Step 06
Commit the Fix
Once you approve the solution, instruct the AI to open a pull request. This prepares the change for team review and merging into the main codebase.
Example prompt
This looks correct. Please open a pull request with the title "[PR Title]" and the following description: [Describe the fix and link to the original issue].
What good looks like
- New test reproduces the reported bug before the fix
- Updated tests pass after the fix is applied
- Pull request references the original GitHub issue
- Human reviewer approves the code and regression coverage
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Runbook notes
How to recover when the loop fails and where human judgment helps.
If it goes sideways
- Regression test does not accurately reproduce the customer issue
- Match the test inputs and environment to the issue report details
- Fix passes the new test but breaks unrelated functionality
- Run the full repository test suite and inspect failing areas
- AI changes unrelated files while implementing the fix
- Restrict the PR scope to files connected to the failing test
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