Section 141 · Chapter 17, Personalized and Dynamic AI Products
Testing Personalization Lock-In and Portability
Personalization becomes infrastructure when users cannot leave without losing the AI that understands them.
personalizationpersonalization lock portability
What to do
- Measure degradation after migration rather than assuming exported data means exported quality.
- Define runnable checks that exercise personalization and personalization lock portability.
- Set acceptable outcomes and blocker failures for personalization and personalization lock portability before running the evaluation.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for personalization, personalization lock portability needed to reproduce work on Testing Personalization Lock-In and Portability.
- Report results for personalization, personalization lock portability by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
The deeper move is that portability testing needs export completeness, schema stability, import fidelity, behavior-parity evals, privacy filtering, consent preservation, and rollback plans. Measure degradation after migration rather than assuming exported data means exported quality.
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Cite this page
Jason Arbon. "Testing Personalization Lock-In and Portability." Testing AI Knowledge Edition, section 141.
https://jarbon.ai/testing-ai/knowledge/ch141-personalization-lock-portability.html