Debugging AI Systems: A Pragmatic Approach
Start with the inputs
AI systems fail when inputs deviate from training data. Log and analyze incoming prompts—look for outliers or unexpected patterns.
Monitor outputs, not just metrics
A model’s accuracy score doesn’t tell the full story. Track user-facing behavior: Are responses relevant? Are they consistent over time?
Isolate the failure
Narrow the scope: Is the issue in data preprocessing, model inference, or post-processing? Use controlled tests to reproduce the problem before fixing it.