AI configuration defines the approved instructions, data access, models, thresholds and controls that shape how an AI feature behaves in a business workflow. It is the operating setup around the model, not the model alone. Configuration changes can alter outputs even when the underlying model version stays the same.
What can AI configuration include?
- System instructions, task prompts and allowed actions
- Data sources, retrieval scope and permissions
- Model and version selection
- Confidence thresholds and escalation rules
- Output format, terminology and validation
- Logging, retention and human-review requirements
How should configuration be governed?
Assign an owner, version changes, test against realistic cases and require approval proportional to the decision risk. Preserve the configuration used for material outputs so reviewers can distinguish a data change from a prompt, rule or model change.
Configuration vs. customization
Configuration changes supported settings and instructions. Customization can include new code, integrations, training or model adaptation. The boundary depends on the product, but both require testing and change records.
What should be tested?
Test expected cases, missing data, conflicting instructions, restricted content, edge cases and escalation. A successful demonstration is not evidence that the feature is reliable across the production population.
