How does AI help with debugging beyond just explaining errors?

AI acts as a force multiplier for logic. Instead of reading logs manually, you learn to use 'Systematic Debug Prompting'—providing specific context stacks and tracing logic to force the AI to find the needle in the haystack.

Does this work with production errors?

Yes. We teach you how to sanitize logs and provide trace context safely so you can use AI to identify patterns in production failures without leaking sensitive data.

Chapter 09 Deep Dive

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debugger.sh

The Debugging Gap

Traditional Debugging

✗ Staring at logs for 30 minutes hoping for a 'eureka' moment.
✗ Random console.log statements across the repo.
✗ Searching StackOverflow for error messages that don't match your context.

AI-Accelerated Debugging

✓ Systematic Prompting: Feeding the right trace context for instant answers.
✓ Error Analysis: Turning cryptic stack traces into plain English logic flaws.
✓ Fix Verification: Using AI to predict if a fix will break other parts of the system.

What You'll Master in Chapter 09

🔍

The Context Stack

Learn which 3 files are essential for any debug session and how to feed them to AI without hitting token limits.

🧱

Systematic Prompting

A repeatable 4-step loop to narrow down a bug: Discovery, Hypothesis, Isolation, and Implementation.

🛡️

Legacy Archaeology

Using AI to explain weird logic in old codebases so you can fix it safely without the "chesterton's fence" problem.

FAQ: AI Debugging

No. While we use Cursor for many examples, the systematic debugging frameworks transfer perfectly to ChatGPT, Claude, or GitHub Copilot. It's about how you think, not the tool.

Yes. Chapter 9 specifically covers how to spot when an AI is 'guessing' a fix and how to pivot the conversation back to the source code truth.

Most seniors spend hours debugging. If you aren't using AI to reconstruct traces and isolate state variables yet, this chapter alone will save you 10+ hours a month.

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