You can’t debug what you can’t observe.
In GenAI systems, observability is harder and more important.
Why traditional metrics fall short
Latency and error rates still matter.
But they don’t tell you:
- Whether answers are correct
- Whether behavior drifted
- Whether users trust the output
Correctness is qualitative, not binary.
What needs to be observed
Effective GenAI systems track:
- Prompt and retrieval versions
- Input-output pairs
- Model versions
- Token usage
- Failure modes
This creates traceability across behavior changes.
Evaluation as a continuous process
GenAI systems cannot be “tested once.”
They require:
- Representative datasets
- Regression checks
- Periodic re-evaluation
- Human-in-the-loop review
Evaluation becomes part of operations, not a pre-release step.
Why this changes engineering culture
Teams stop asking:
- “Does it work?”
They start asking:
- “How is it behaving now?”
That shift is subtle, but it defines mature GenAI teams.
The final post looks at what this all means for engineers moving into GenAI roles.
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