Surviving Model Deprecations: Building Provider-Agnostic AI Apps

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-model-families-AI-model-deprecation-policy-creators

1. What is the main idea of "Surviving Model Deprecations: Building Provider-Agnostic AI Apps"?

   1. How providers deprecate models and what your code needs to look like to survive it.
   2. Use AI as the final authority for the whole decision
   3. Avoid checking the answer once it sounds polished
   4. Focus only on speed instead of judgment

2. Which concept is most central to "Surviving Model Deprecations: Building Provider-Agnostic AI Apps"?

   1. deprecation
   2. model versioning
   3. provider-agnostic
   4. migration

3. Which use of AI fits this topic best?

   1. Pin a model forever — providers will sunset old versions
   2. Let the AI decide what matters without your review
   3. Centralize model name as a config variable, never a literal
   4. Use the answer before checking whether it fits the situation

4. Which limitation should you watch for in this topic?

   1. Centralize model name as a config variable, never a literal
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Pin a model forever — providers will sunset old versions

5. What should a careful learner remember about "Model migration runbook"?

   1. Use AI to draft or organize ideas about model versioning, then verify before acting.
   2. Skip the context so the tool can guess faster
   3. Treat the output as private even after sharing it online
   4. Use the answer without checking the source

6. You want to use AI after this lesson. What is the safest next step?

   1. Act immediately because the AI answer is written clearly
   2. Use AI for drafting and comparison, but verify before publishing or relying on it.
   3. Hide uncertainty so the final answer looks cleaner
   4. Use private or sensitive details before checking permission

7. How should AI output about model versioning be treated?

   1. As proof that no other source is needed
   2. As a replacement for context, consent, or expert review
   3. As a draft or helper output that still needs human judgment and verification
   4. As something that becomes correct when it sounds confident

8. Name one way to verify an AI answer about model versioning.

9. Which action would help you apply "Surviving Model Deprecations: Building Provider-Agnostic AI Apps" responsibly?

   1. Migrate without behavior change — every model has its own quirks
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Run an eval set every time you change models

10. Which choice is a bad use of AI for this lesson?

    1. Migrate without behavior change — every model has its own quirks
    2. Centralize model name as a config variable, never a literal
    3. Ask for a plain-language explanation of deprecation
    4. Compare the answer with a trusted source