AI documentation should explain more than how a model was built. It should describe how the model behaves when inputs, data, and operating conditions change.
Define Expected Behavior
Document the model’s purpose, accepted inputs, expected outputs, and normal operating conditions. Connect technical performance to the situations in which people will use the system.
Record Limitations and Failures
Describe known limitations, inaccurate predictions, hallucinations, inconsistent responses, and other failure patterns. Include the conditions that cause these problems so developers can reproduce and investigate them.
Explain Input Sensitivity
Model outputs may change because of prompt wording, missing data, retrieved context, configuration settings, or model updates. Recording these effects makes testing and troubleshooting easier.
Keep the Documentation Current
Update behavioral documentation when testing or production monitoring reveals new failures, performance changes, or model drift. Treat it as part of the system rather than a one-time deliverable.
Clear behavioral documentation helps teams test, deploy, and maintain more dependable AI applications.
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