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Claude Code in Your Development Flow

Lesson 3: Debugging and Troubleshooting with Claude Code

Lesson Objectives

By the end of this lesson, students should be able to:

  • Apply the behavior-first debugging protocol with Claude Code
  • Share logs and error traces effectively for AI-assisted diagnosis
  • Use Claude Code to generate hypotheses and verify them methodically
  • Recognize when debugging requires human expert knowledge Claude lacks

Lesson Content

The behavior-first debugging protocol.

Step 1 – Define the failure precisely:

  • What is the observed behavior? (exact error, wrong output, wrong state)
  • What is the expected behavior?
  • Under what input or conditions does the failure occur?
  • Is the failure consistent or intermittent?

Step 2 – Share relevant context:

  • The code path that handles the failing case (file + function or component)
  • The error trace or logs (full stack trace where available)
  • Any recent changes (what changed before the failure appeared?)

Step 3 – Ask for hypotheses, not fixes:

  • "What are the most likely causes of this behavior given this code and this error?"
  • Review hypotheses against your knowledge of the system
  • Test the most likely hypothesis before asking for a fix

This sequence produces diagnosis. Skipping to "fix this" produces guesses.

Log and error trace analysis.

Claude Code reads logs and stack traces effectively. When sharing:

  • Include the full error message and stack trace – not just the error type
  • Include the relevant lines of code, not just the trace
  • For async errors, include the call site and the error location

Ask Claude to interpret a stack trace before asking for a fix: "What does this stack trace tell us about where the failure occurred and what caused it?" This produces understanding, not just a patch.

Hypothesis testing workflow.

For each hypothesis Claude generates:

  1. Evaluate: does this hypothesis match the symptoms you observed?
  2. Design a test: how would you verify this hypothesis?
  3. Test: make the minimal change to test the hypothesis
  4. Evaluate: did the behavior change as predicted?

This is standard scientific debugging. Claude accelerates step 1 (generating hypotheses) – the developer still tests and evaluates.

When debugging requires human expert knowledge.

Claude is well-suited for: common bug patterns, syntax errors, async/await misuse, off-by-one errors, type errors, common library misuse.

Claude is less reliable for: performance profiling in specific hardware or runtime contexts, database query optimization for specific data distributions, distributed systems race conditions, domain-specific edge cases with limited training data coverage.

For performance and distributed systems bugs, Claude's hypotheses are a starting point – not a conclusion.

Practical Example

A developer has a race condition in a Node.js application: occasionally, a user session is not found immediately after being created.

She runs the behavior-first protocol: observed behavior (session not found 3% of requests), expected behavior (session always found), condition (only occurs under concurrent request load).

She shares the session creation and lookup code with Claude.

Claude generates three hypotheses: (1) async gap between write and index update, (2) read replica lag in database, (3) in-memory cache invalidation issue.

She tests hypothesis 2 first (most likely given the infrastructure) – replaces the read replica lookup with the primary – and the 3% failure disappears.

Total time: twenty minutes.

The hypothesis generation accelerated diagnosis; she did the testing and evaluation.

Safety Notes

When sharing code for debugging, be mindful of what you are sharing. Code that contains hardcoded credentials, production database connection strings, API keys, or proprietary business logic should be sanitized before sharing with any external tool. Claude Code processes content through Anthropic's API. Review Anthropic's data handling policies at anthropic.com/privacy before sharing sensitive code.

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