AI and embedding model selection

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-model-families-AI-and-embedding-model-selection-r9a1-creators

1. What is the main idea of "AI and embedding model selection"?

   1. Embedding models differ on dimension, language coverage, and recall — pick by your retrieval task, not by leaderboard.
   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 and embedding model selection"?

   1. MTEB
   2. embedding
   3. dimension
   4. language coverage

3. Which use of AI fits this topic best?

   1. Predict recall on your data without testing.
   2. Let the AI decide what matters without your review
   3. Suggest a small in-domain eval.
   4. Use the answer before checking whether it fits the situation

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

   1. Suggest a small in-domain eval.
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Predict recall on your data without testing.

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

   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 and embedding model selection" responsibly?

   1. Replace re-embedding cost when you switch.
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Compare on: dimension, languages, recall@k.

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

    1. Replace re-embedding cost when you switch.
    2. Suggest a small in-domain eval.
    3. Ask for a plain-language explanation of MTEB
    4. Compare the answer with a trusted source