Evaluation suite fundamentals: what to measure and how

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-eval-suite-fundamentals

1. What is the core idea behind "Evaluation suite fundamentals: what to measure and how"?

   1. Build an eval suite that mixes deterministic checks, LLM-as-judge, and human review — knowing each one's limits.
   2. Most AIs learned from the internet up to a certain date and then stopped.
   3. Pass through an activation function like ReLU or sigmoid
   4. Cache known-good outputs and fall through to LLM only on cache miss

2. Which term best describes a foundational idea in "Evaluation suite fundamentals: what to measure and how"?

   1. LLM as judge
   2. deterministic eval
   3. human eval
   4. regression set

3. A learner studying Evaluation suite fundamentals: what to measure and how would need to understand which concept?

   1. deterministic eval
   2. human eval
   3. LLM as judge
   4. regression set

4. Which of these is directly relevant to Evaluation suite fundamentals: what to measure and how?

   1. deterministic eval
   2. LLM as judge
   3. regression set
   4. human eval

5. Which of the following is a key point about Evaluation suite fundamentals: what to measure and how?

   1. Design a tiered eval suite with appropriate cost per tier.
   2. Draft regression-set hygiene rules to prevent eval rot.
   3. Most AIs learned from the internet up to a certain date and then stopped.
   4. Pass through an activation function like ReLU or sigmoid

6. What is one important takeaway from studying Evaluation suite fundamentals: what to measure and how?

   1. Eliminate the maintenance cost of eval suites.
   2. Replace human review for subjective qualities.
   3. Most AIs learned from the internet up to a certain date and then stopped.
   4. Pass through an activation function like ReLU or sigmoid

7. What is the key insight about "Tiered eval suite design" in the context of Evaluation suite fundamentals: what to measure and how?

   1. Most AIs learned from the internet up to a certain date and then stopped.
   2. Pass through an activation function like ReLU or sigmoid
   3. Design a three-tier eval suite for our product: deterministic (every commit), LLM-as-judge (nightly), human (weekly).
   4. Cache known-good outputs and fall through to LLM only on cache miss

8. What is the key insight about "Judge models inherit judge bias" in the context of Evaluation suite fundamentals: what to measure and how?

   1. Most AIs learned from the internet up to a certain date and then stopped.
   2. Pass through an activation function like ReLU or sigmoid
   3. Cache known-good outputs and fall through to LLM only on cache miss
   4. LLM-as-judge has its own preferences. Spot-check judge agreement with humans regularly or your scores drift quietly.

9. What is the recommended tip about "Ground your practice in fundamentals" in the context of Evaluation suite fundamentals: what to measure and how?

   1. Every AI capability has an underlying mechanism. Understanding that mechanism tells you where it'll fail — which is more…
   2. Most AIs learned from the internet up to a certain date and then stopped.
   3. Pass through an activation function like ReLU or sigmoid
   4. Cache known-good outputs and fall through to LLM only on cache miss

10. Which statement accurately describes an aspect of Evaluation suite fundamentals: what to measure and how?

    1. Most AIs learned from the internet up to a certain date and then stopped.
    2. A real eval suite combines fast deterministic checks, mid-cost judge models, and slow human review; each layer covers what the others miss.
    3. Pass through an activation function like ReLU or sigmoid
    4. Cache known-good outputs and fall through to LLM only on cache miss

11. Which best describes the scope of "Evaluation suite fundamentals: what to measure and how"?

    1. It is unrelated to foundations workflows
    2. It applies only to the opposite beginner tier
    3. It focuses on Build an eval suite that mixes deterministic checks, LLM-as-judge, and human review — knowing each o
    4. It was deprecated in 2024 and no longer relevant

12. Which section heading best belongs in a lesson about Evaluation suite fundamentals: what to measure and how?

    1. Most AIs learned from the internet up to a certain date and then stopped.
    2. Pass through an activation function like ReLU or sigmoid
    3. Cache known-good outputs and fall through to LLM only on cache miss
    4. What AI does well here

13. Which section heading best belongs in a lesson about Evaluation suite fundamentals: what to measure and how?

    1. What AI cannot do
    2. Most AIs learned from the internet up to a certain date and then stopped.
    3. Pass through an activation function like ReLU or sigmoid
    4. Cache known-good outputs and fall through to LLM only on cache miss

14. Which of the following is a concept covered in Evaluation suite fundamentals: what to measure and how?

    1. LLM as judge
    2. deterministic eval
    3. human eval
    4. regression set

15. Which of the following is a concept covered in Evaluation suite fundamentals: what to measure and how?

    1. deterministic eval
    2. human eval
    3. LLM as judge
    4. regression set