AI Model Evals: How to Test a New Release in 30 Minutes

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-model-families-AI-evaluating-new-models-r13a3-creators

1. What is the primary purpose of a 'golden set' in model evaluation?

   1. A standard academic benchmark published by AI research labs
   2. A curated collection of prompts with known good answers used to benchmark new models
   3. A secret test designed to trick AI models into making mistakes
   4. A list of all possible user queries the model might ever encounter

2. Why does the lesson recommend that eval prompts come from your 'real workload'?

   1. To make the evaluation cheaper than using synthetic prompts
   2. So the test results reflect the actual tasks your application needs the model to perform
   3. To ensure the new model sees examples it has never been trained on
   4. To match the format of published academic benchmarks

3. In a proper blind evaluation, the grader should:

   1. Evaluate responses without seeing the original prompts
   2. Grade responses as quickly as possible without reviewing them twice
   3. Grade responses without knowing what the correct answer should be
   4. Not know which model generated each response being evaluated

4. What does the lesson mean by deciding 'on numbers, not vibes'?

   1. Use measurable quality scores and metrics rather than subjective impressions
   2. Rely on the model's self-reported confidence scores
   3. Only trust numerical benchmarks and ignore any qualitative assessment
   4. Calculate your gut feelings mathematically before making decisions

5. In the context of this lesson, what is a 'regression test' for AI models?

   1. Testing whether the model can detect bugs in other software
   2. Running the model multiple times to find inconsistent outputs
   3. Checking that a new model doesn't perform worse than the old one on known-good tasks
   4. Comparing a model's output against human-written reference answers

6. Which metric is NOT mentioned in the lesson as something to track alongside quality when evaluating a model?

   1. User satisfaction ratings
   2. Latency
   3. Cost
   4. Refusal rate

7. Why can't a 30-minute evaluation replace long-term production monitoring?

   1. Evaluating in production is faster than running tests
   2. Production data distribution changes over time in ways a static eval can't predict
   3. AI models continue learning after deployment
   4. Production monitoring doesn't provide useful metrics

8. What does 'data drift' refer to in model evaluation?

   1. The model becoming slower as it processes more requests
   2. Errors accumulating in the model's outputs as it runs longer
   3. Changes in the distribution of data your model processes in production over time
   4. The model size increasing after fine-tuning

9. What makes a 30-minute eval possible without a research lab?

   1. Testing only the simplest possible prompts
   2. Using a pre-existing golden set of 50 prompts rather than creating new ones each time
   3. Using automated evaluation instead of human graders
   4. Running evaluations on multiple computers simultaneously

10. What does 'refusal rate' measure in model evaluation?

    1. The rate at which the model rejects user feedback
    2. The frequency with which the model produces incorrect outputs
    3. The percentage of prompts the model declines to answer due to safety concerns
    4. The number of times the model fails to generate any output

11. Why is it important to run both models on the exact same golden set?

    1. To make statistical comparison valid
    2. To ensure neither model has an unfair advantage
    3. To speed up the evaluation process
    4. Differences in results will then reflect model capability differences, not prompt variation

12. What is 'latency' in the context of model evaluation?

    1. The time delay between submitting a prompt and receiving a complete response
    2. The total computational cost of running the model
    3. The length of the model's output in tokens
    4. The model's accuracy on benchmark datasets

13. In this lesson, what does 'model swap' refer to?

    1. Replacing your current production model with a new candidate model
    2. Running two models simultaneously and averaging their outputs
    3. Adding a new model to work alongside your existing one
    4. Changing the underlying architecture of an existing model

14. Why should a colleague (not you) perform the blind grading?

    1. Because your colleagues are more objective about AI capabilities
    2. To ensure the grading follows proper scientific methodology
    3. To save you time for other work
    4. To prevent your expectations from biasing the quality judgment of responses

15. What is wrong with deciding a model is better based on how it 'feels' during use?

    1. Feelings are always accurate predictors of model performance
    2. Subjective impressions can be misled by a single impressive response; numbers provide objective evidence
    3. Numerical benchmarks cannot capture important aspects of intelligence
    4. Impressive responses are the most reliable indicator of overall quality