AI model families: roadmap watching without thrash

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-model-families-roadmap-watching-r7a1-creators

1. What is the core problem that a disciplined upgrade cadence aims to solve?

   1. AI providers deliberately retire models to force expensive migrations
   2. New AI models release frequently enough that evaluating every release creates excessive overhead
   3. Upgrading too infrequently causes performance degradation in production systems
   4. Constant model upgrades prevent developers from building meaningful features

2. Why should production AI calls be pinned to a specific dated snapshot rather than always using the latest available model version?

   1. Dated snapshots provide consistency and reproducibility across production calls
   2. The latest model version is always more expensive than older snapshots
   3. Pinning prevents the AI provider from charging for usage
   4. Pinned snapshots guarantee the model will produce identical outputs forever

3. What does the lesson identify as something AI systems can reliably do to support upgrade discipline?

   1. Predict which model will be released six months from now
   2. Surface measurable performance differences between snapshots when tested on the same eval set
   3. Warn developers before their pinned snapshot gets deprecated
   4. Automatically rewrite prompts to work with new model versions

4. What is the recommended frequency for conducting formal evaluations of new model snapshots against your existing baseline?

   1. Whenever a model shows a major version number increase
   2. Every time your production error rate exceeds 5%
   3. Once per quarter, using a consistent evaluation set
   4. As soon as any new model is released by any provider

5. What does the lesson say AI cannot do, regardless of how sophisticated the model is?

   1. Generate text that is grammatically correct
   2. Process requests faster than their advertised latency
   3. Tell you whether a new model is worth migrating to without your own evaluation data
   4. Understand context longer than 8,000 tokens

6. Why must migration decisions be based on your own evaluation data rather than provider claims or third-party benchmarks?

   1. Third-party benchmarks use eval sets designed to make expensive models look better
   2. Provider claims are always false and deliberately misleading
   3. Legal requirements mandate that companies use their own evaluation data
   4. AI behavior varies across different use cases, so your specific eval set reveals whether a new snapshot actually improves your workload

7. What risk exists when relying on any specific model snapshot for production systems?

   1. The AI provider may retire or deprecate that snapshot on their own schedule
   2. The snapshot may stop working if you change your API key
   3. The snapshot may be slower than alternatives
   4. The snapshot might become more expensive over time

8. What should you prepare before a model snapshot you depend on reaches its deprecation date?

   1. A complaint filed with the AI provider's support team
   2. A documented migration path to a replacement snapshot
   3. A request for an indefinite extension on the deprecated snapshot
   4. A detailed explanation of why you chose that specific snapshot

9. What characteristic of prompts makes migration between model snapshots smoother?

   1. Prompts that include detailed style guidelines for every possible edge case
   2. Prompts that are as short as possible, ideally under 10 words
   3. Prompts that explicitly reference the model name and version number
   4. Prompts written in a portable, implementation-agnostic way

10. If a new model snapshot scores 8% better on your evaluation set but costs 3x as much per request, what does the lesson recommend?

    1. Wait until the new snapshot becomes cheaper before evaluating
    2. Do not migrate because any cost increase is unacceptable
    3. Migrate immediately because performance gains always justify higher costs
    4. Migrate only if cost and latency are acceptable in addition to the performance gain

11. The lesson describes which of the following as a key trade-off in model upgrade decisions?

    1. Between upgrade frequency and migration cost
    2. Between model speed and model intelligence
    3. Between training data size and inference speed
    4. Between prompt length and output quality

12. What does the lesson say about behavior preservation when moving between model versions?

    1. AI cannot preserve all prior behaviors across versions, and some changes are expected
    2. Behavior preservation is guaranteed if you use the same prompt
    3. Models are designed to maintain identical behavior across all versions
    4. Moving between snapshots within the same family always preserves behavior

13. What is a 'snapshot' in the context of AI model families, as described in this lesson?

    1. A specific dated version of a model that remains available for a period
    2. A summary of model capabilities generated by the provider
    3. A saved copy of your conversation history with the AI
    4. A backup of your evaluation dataset

14. Why is the quarterly evaluation cadence recommended rather than evaluating on a different schedule?

    1. Annual evaluations are too infrequent but monthly are too expensive
    2. Quarterly evaluations align with the approximately monthly model release pace, providing enough time to build while staying reasonably current
    3. Quarterly is required by most AI provider terms of service
    4. Evaluations conducted more frequently would damage the model

15. What happens if you upgrade to a new model snapshot every time one is released, without any discipline?

    1. The AI provider gives you a discount for loyalty
    2. You spend more time re-evaluating than actually building features
    3. Your production systems become more secure against attacks
    4. Your prompts automatically become more portable