Debugging with AI: How I Cut Error Fix Time by 70%

Every developer knows the pain of debugging:

• Endless logs
• Cryptic error messages
• Hours wasted on Stack Overflow

I used to spend half my day chasing down bugs. But once I started using AI systematically, I cut my error-fix time by 70%.

Here’s exactly how I did it.

1️⃣ Explaining Error Logs

Most error logs are written for machines, not humans. AI can translate them into plain English.

💡 Prompt Example:

“Here’s my error log: [paste]. Explain the issue in simple terms and suggest likely causes.”

Why: You understand the bug’s root cause in minutes, not hours.

2️⃣ Generating Fixes with Context

Instead of patching blindly, I feed both the error and the relevant code snippet to AI.

💡 Prompt Example:

“This function throws an error: [paste code]. Suggest 2 fixes, explain pros/cons, and rewrite the corrected version.”

Why: AI gives multiple solutions, helping me choose the best one for my use case.

3️⃣ Creating Unit Tests to Prevent Recurrence

Once a bug is fixed, I don’t want it coming back. AI helps by generating test cases that specifically target past errors.

💡 Prompt Example:

“Generate 3 unit tests to ensure this bug doesn’t appear again in [language/framework].”

Why: Fix once, protect forever.

4️⃣ Optimising Debugging Workflow

Instead of searching Google + forums, I now:

• Paste the error into AI
• Get an explanation + fixes
• Validate with tests
• Document the solution instantly

This reduces my context switching — the biggest hidden time drain.

5️⃣ Documenting the Fix

AI makes it easy to capture what happened for future me (or teammates).

💡 Prompt Example:

“Write a documentation note explaining the error, the fix applied, and how to avoid it in future.”

Why: Next time, I don’t start from scratch.

Results

• Before: A tough bug might take 3–4 hours.
• Now: The same bug takes 45 minutes or less.

That’s not just a productivity boost — it’s a mindset shift.
Debugging went from frustrating to systematic and manageable.

More Learning Resources:

• Prompt Books → Ready-to-use libraries across business, authorship, productivity, and branding → ChatGPT Prompts Access
• My live lectures on prompts & productivity → ReThynk AI YouTube Channel
• Plug-and-play prompt systems (free & paid) → ReThynk AI Templates & Frameworks
• Professional AI, business, and tech insights (currently free on our website) → ReThynk AI Magazine

📌 Next Post: “AI-Powered Code Reviews: A Developer’s Secret Weapon” — how to use AI as your 24/7 code review partner.