AI Model Families: Pick an Embedding Model You Can Live With

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-model-families-embeddings-pick-r8a1-creators

1. What is the main idea of "AI Model Families: Pick an Embedding Model You Can Live With"?

   1. Embedding choice is hard to reverse — re-embedding millions of documents is expensive — so optimize for retrieval.
   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 "AI Model Families: Pick an Embedding Model You Can Live With"?

   1. recall@k
   2. embedding
   3. re-embed cost
   4. hybrid retrieval

3. Which use of AI fits this topic best?

   1. Predict provider price or deprecation
   2. Let the AI decide what matters without your review
   3. Build a small retrieval-quality test from real queries
   4. Use the answer before checking whether it fits the situation

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

   1. Build a small retrieval-quality test from real queries
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Predict provider price or deprecation

5. What should a careful learner remember about "Prompt: embedding eval"?

   1. Use AI to draft or organize ideas about embedding, 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 embedding 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 embedding.

9. Which action would help you apply "AI Model Families: Pick an Embedding Model You Can Live With" responsibly?

   1. Replace tuning your chunking strategy
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Score candidates on recall@k for your data

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

    1. Replace tuning your chunking strategy
    2. Build a small retrieval-quality test from real queries
    3. Ask for a plain-language explanation of recall@k
    4. Compare the answer with a trusted source