Distillation Tradeoffs: When Smaller Models Quietly Lose

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-distillation-tradeoffs-foundations

1. What is the core idea behind "Distillation Tradeoffs: When Smaller Models Quietly Lose"?

   1. Distilled models look great on aggregate evals but quietly lose long-tail capabilities — the tradeoff matrix matters for production decisions.
   2. capability gap
   3. GPT-4o input = $2.50 per million tokens; one essay ≈ 800 tokens
   4. GPT-5, Claude Opus 4.7, Gemini 3, Llama 4 — they're not interchangeable.

2. Which term best describes a foundational idea in "Distillation Tradeoffs: When Smaller Models Quietly Lose"?

   1. long-tail capability
   2. distillation
   3. eval coverage
   4. regression testing

3. A learner studying Distillation Tradeoffs: When Smaller Models Quietly Lose would need to understand which concept?

   1. distillation
   2. eval coverage
   3. long-tail capability
   4. regression testing

4. Which of these is directly relevant to Distillation Tradeoffs: When Smaller Models Quietly Lose?

   1. distillation
   2. long-tail capability
   3. regression testing
   4. eval coverage

5. Which of the following is a key point about Distillation Tradeoffs: When Smaller Models Quietly Lose?

   1. Draft tradeoff matrices comparing teacher vs distilled across capability dimensions.
   2. Generate long-tail eval prompts to surface hidden regressions.
   3. capability gap
   4. GPT-4o input = $2.50 per million tokens; one essay ≈ 800 tokens

6. What is one important takeaway from studying Distillation Tradeoffs: When Smaller Models Quietly Lose?

   1. Replace production shadow-traffic testing.
   2. Decide acceptable capability loss for your workload.
   3. capability gap
   4. GPT-4o input = $2.50 per million tokens; one essay ≈ 800 tokens

7. What is the key insight about "Distillation tradeoff matrix" in the context of Distillation Tradeoffs: When Smaller Models Quietly Lose?

   1. capability gap
   2. GPT-4o input = $2.50 per million tokens; one essay ≈ 800 tokens
   3. Draft a tradeoff matrix comparing a teacher and a distilled student.
   4. GPT-5, Claude Opus 4.7, Gemini 3, Llama 4 — they're not interchangeable.

8. What is the key insight about "Aggregate scores hide regressions" in the context of Distillation Tradeoffs: When Smaller Models Quietly Lose?

   1. capability gap
   2. GPT-4o input = $2.50 per million tokens; one essay ≈ 800 tokens
   3. GPT-5, Claude Opus 4.7, Gemini 3, Llama 4 — they're not interchangeable.
   4. A distilled model 2 points behind on aggregate may be 30 points behind on a niche capability your top customer depends o…

9. Which statement accurately describes an aspect of Distillation Tradeoffs: When Smaller Models Quietly Lose?

   1. AI can frame distillation tradeoffs and design eval coverage, but actual production decisions need workload-specific testing.
   2. capability gap
   3. GPT-4o input = $2.50 per million tokens; one essay ≈ 800 tokens
   4. GPT-5, Claude Opus 4.7, Gemini 3, Llama 4 — they're not interchangeable.

10. Which best describes the scope of "Distillation Tradeoffs: When Smaller Models Quietly Lose"?

    1. It is unrelated to foundations workflows
    2. It focuses on Distilled models look great on aggregate evals but quietly lose long-tail capabilities — the tradeof
    3. It applies only to the opposite beginner tier
    4. It was deprecated in 2024 and no longer relevant

11. Which section heading best belongs in a lesson about Distillation Tradeoffs: When Smaller Models Quietly Lose?

    1. capability gap
    2. GPT-4o input = $2.50 per million tokens; one essay ≈ 800 tokens
    3. What AI does well here
    4. GPT-5, Claude Opus 4.7, Gemini 3, Llama 4 — they're not interchangeable.

12. Which section heading best belongs in a lesson about Distillation Tradeoffs: When Smaller Models Quietly Lose?

    1. capability gap
    2. GPT-4o input = $2.50 per million tokens; one essay ≈ 800 tokens
    3. GPT-5, Claude Opus 4.7, Gemini 3, Llama 4 — they're not interchangeable.
    4. What AI cannot do

13. Which of the following is a concept covered in Distillation Tradeoffs: When Smaller Models Quietly Lose?

    1. distillation
    2. long-tail capability
    3. eval coverage
    4. regression testing

14. Which of the following is a concept covered in Distillation Tradeoffs: When Smaller Models Quietly Lose?

    1. distillation
    2. long-tail capability
    3. eval coverage
    4. regression testing

15. Which of the following is a concept covered in Distillation Tradeoffs: When Smaller Models Quietly Lose?

    1. distillation
    2. long-tail capability
    3. eval coverage
    4. regression testing