AI and Eval Harness Design: Building Your Own Test Set

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-foundations-AI-and-eval-harness-design-r11a4-creators

1. What is the primary reason a creator might build a custom eval harness instead of using existing benchmarks?

   1. Off-the-shelf benchmarks are always more expensive to implement than custom solutions
   2. Standard benchmarks have been proven to be more accurate for all domains
   3. Pre-made eval sets require no technical knowledge to interpret
   4. Existing benchmarks typically evaluate general capabilities rather than specific use cases relevant to the creator's project

2. A developer wants to test whether their AI application handles toxic inputs appropriately. Which task would best represent an adversarial test case?

   1. Comparing outputs against a competitor's API responses
   2. Measuring response time across different server loads
   3. Running the system on a representative sample of normal user queries
   4. Inputting deliberately crafted prompts designed to trigger harmful or undesirable outputs

3. Which of the following is a task AI can reliably perform when designing an evaluation harness?

   1. Guaranteeing that a model will pass the eval on the first attempt
   2. Automatically determining whether a model will perform well on inputs never seen during design
   3. Drafting eval categories based on a provided set of sample inputs
   4. Replacing human judgment when evaluating subjective qualities like humor or creativity

4. A scoring rubric in an eval harness serves which purpose?

   1. It automatically fixes errors in model responses
   2. It determines which hardware the model runs on during testing
   3. It generates new test cases automatically
   4. It provides a set of predefined criteria for evaluating model outputs consistently

5. Why is human judgment still necessary when evaluating outputs from subjective tasks?

   1. Subjective tasks require understanding context, nuance, and intent that AI struggles to assess reliably
   2. AI always produces perfect scores for subjective tasks
   3. Humans are faster at evaluating outputs than automated systems
   4. AI systems cannot legally evaluate subjective content due to copyright restrictions

6. What does it mean when someone says a model has 'gamed' an eval set?

   1. The model has learned to pass the specific test set without actually developing the underlying capability being measured
   2. The test set contains too many difficult examples
   3. The model has been trained on too few examples
   4. The evaluation harness is running too slowly to measure performance accurately

7. A creator refreshes their eval test set every three months. What problem are they specifically trying to prevent?

   1. The test set size becoming too small to be statistically significant
   2. Models becoming too accurate on the test set
   3. Models gaming the eval by overfitting to the specific test cases
   4. The test set becoming outdated due to new model architectures

8. Which limitation of AI in eval design makes it impossible to create a perfect eval before deployment?

   1. AI cannot predict performance on inputs it hasn't sampled
   2. AI cannot identify the correct programming language for the harness
   3. AI cannot generate enough test cases
   4. AI cannot format a scoring rubric properly

9. A company builds an eval harness to test an AI assistant for customer service. What is the most important factor in determining whether this eval is effective?

   1. How expensive the eval was to develop
   2. How many different AI models the eval can compare
   3. How quickly the eval completes its tests
   4. Whether the test cases reflect realistic customer service scenarios

10. What is the fundamental challenge that prevents AI from fully automating eval harness creation?

    1. AI cannot replace human judgment on subjective tasks and cannot predict performance on unseen inputs
    2. AI cannot access the internet to find benchmark data
    3. AI cannot write code for the harness
    4. AI cannot generate any test cases

11. A creator provides 50 sample tasks to an AI and asks it to draft an eval harness. What should the creator expect the AI to produce?

    1. A list of competitors who have built similar eval harnesses
    2. A fully finished, production-ready eval that requires no human involvement
    3. A draft including eval categories, a scoring rubric, and potential adversarial test cases
    4. A final report determining whether their model is ready for deployment

12. Why might two different creators need completely different eval harnesses for the same type of AI model?

    1. One creator is using a newer AI model than the other
    2. Government regulations require different evals for each creator
    3. They are building different applications with different quality criteria for success
    4. All AI models require unique evals regardless of use case

13. What is the relationship between the 'quality' of an eval harness and its design process?

    1. A quality eval must measure what actually matters for the specific use case, not just general benchmarks
    2. Quality is determined solely by how many test cases are included
    3. Quality is irrelevant if the eval runs automatically
    4. Higher quality evals always require more expensive AI tools

14. What is the minimum human involvement required in a properly designed eval harness?

    1. Humans only need to build the harness once and never interact again
    2. Only initial setup requires humans; AI handles ongoing evaluation
    3. No human involvement is needed—AI handles everything
    4. Human judgment is needed for subjective tasks and to interpret results

15. A developer notices their eval scores have improved dramatically over six months, but real-world performance seems unchanged. What is likely happening?

    1. The real-world users have changed their expectations
    2. The model has become too advanced for the eval to measure
    3. The eval is broken and needs to be deleted
    4. The model has likely gamed the test set by memorizing specific cases