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What Does It Mean to Debug?
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The First Divergence
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Evidence Before Explanation
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The Debugging Stack
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Reading Python Exceptions
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Inspect State, Don't Guess
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Debug the Boundary
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Assertions, Invariants, and Contracts
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Environment Bugs
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The Notebook Is Not the Program You See
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Hidden Notebook State
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Reproducible Notebooks
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Debug the Data Before the Model
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Shapes, Types, Devices, and Tensors
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When Training Goes Wrong
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Debugging Evaluation
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Debugging What You Cannot See
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Is the Model Actually the Problem?
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Inspect the Actual Model Input
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Context Windows and Truncation
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Sampling Is Part of the Program
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Internal Signals
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Representation and Behavioral Diffs
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AI as Builder, Designer, Researcher, and Reviewer
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Debugging Intent
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Debugging Context for Coding Agents
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Debugging AI-Generated Designs
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Debugging AI Research
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Debugging Coding Agents
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Treat Prompts as Programs
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Minimize the Prompt
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Retrieval Is a Pipeline
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Retriever Failure or Generator Failure?
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Debugging Hallucinations
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The Model's Explanation Is Not a Trace
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An Agent Is a Trajectory
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Trace the Agent
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Agent Failure Taxonomy
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Loops, Thrashing, and Retry Storms
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Time Travel, Replay, and Forking
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Causal Replay
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Trajectory Diff
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Multi-Agent Systems
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Can One AI Debug Another?
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The AI Crash Dump
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Diagnostic AI Invariants
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From Symptom to Hypotheses
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Discriminating Experiments
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How Do You Know the Diagnosis Is Right?
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AIDebugBench
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Debug the Debugger
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AI Observability
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From Production Failure to Regression
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Runtime Invariants and Guardrails
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Debugging Cost and Latency
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Debugging in Production
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The Ten-Minute Debug
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The One-Hour Investigation
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The Full AI Incident Investigation
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The Debugging AI Toolkit