Building a Lightweight Eval Harness

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-tools-eval-harness-r12a1-creators

1. A product team notices their model performs well on old test cases but poorly on actual user queries. What does this scenario illustrate about eval case management?

   1. The scorer in the test cases is incorrectly configured
   2. The eval harness is too lightweight and needs a heavier framework
   3. The model has overfit to the training data and needs more parameters
   4. Test cases can become stale and stop reflecting real-world usage patterns, requiring periodic updates

2. Which of these statements correctly describes what an AI evaluation tool can do versus what it cannot determine?

   1. AI tools can run test cases and generate pass/fail results, but they cannot decide which metrics matter for your specific product
   2. AI tools can automatically identify the best model architecture for your needs
   3. AI tools can predict how users will respond to model outputs before deployment
   4. AI tools can measure any quality dimension without you needing to define it

3. What is the purpose of including a 'scorer' component in each test case file?

   1. To programmatically determine whether a model's output meets the expected criteria
   2. To train the model on the specific test case
   3. To automatically fix incorrect model outputs before they are returned
   4. To generate additional test cases based on the input

4. What information should typically be stored in each test case file within an eval harness?

   1. The model's temperature and top-p settings
   2. Only the expected output so the model can memorize it
   3. A random selection of inputs from the internet
   4. The input prompt, the expected correct output, and the scoring logic to evaluate the response

5. Why is it recommended to commit eval reports to version control history?

   1. To reduce the storage size of the test cases
   2. To track performance drift over time and identify when model changes caused regressions
   3. To share results with external customers
   4. To automatically fix bugs in the model

6. A company adds new eval cases monthly and retires old ones that no longer match user queries. What is the main reason for this practice?

   1. To make the test suite pass more often
   2. To reduce the computational cost of running evaluations
   3. To ensure test cases continue to reflect actual product usage and catch real-world failures
   4. To comply with licensing requirements for test data

7. When comparing two model versions on the same eval suite, what can you reliably determine?

   1. Whether the model understands the concepts it outputs
   2. Whether either model is truly intelligent
   3. Which model will perform better on future unseen inputs
   4. Which version performs better on the specific test cases included in the suite

8. What does the lesson mean by saying a 'lightweight eval' can provide 80% of the value?

   1. 80% of teams should use lightweight evals and only 20% need heavier tools
   2. The eval only needs to cover 80% of possible inputs to be useful
   3. The eval will catch 80% of model bugs before deployment
   4. A simple folder of test cases with a small runner script captures most of the benefits of heavy evaluation frameworks

9. If an eval case that previously passed suddenly starts failing, what might this indicate about the model or the case?

   1. Either the model has changed in a way that broke that capability, or the test case no longer represents valid expected behavior
   2. The model is running too slowly
   3. The test case was too easy and should be deleted
   4. The eval harness has a bug and needs to be reinstalled

10. Why is it important that eval test cases reflect 'real use' rather than artificially constructed examples?

    1. The model will refuse to run on artificial examples
    2. If cases don't match how users actually interact with the product, passing them doesn't guarantee real-world performance
    3. Real examples are required by data protection regulations
    4. Artificial examples are too difficult to score

11. What is the relationship between 'metrics' and 'scorers' in an eval harness?

    1. Scorers are only used for text outputs, metrics for numerical outputs
    2. A scorer is code that implements a metric, determining how to evaluate whether a specific test passes or fails
    3. Metrics and scorers are two words for the same thing
    4. Metrics run the model while scorers save the results

12. An eval harness runs a fixed set of test cases and emits pass/fail with diffs. What is the primary value of seeing the 'diff' output?

    1. It automatically corrects the model output
    2. It tells you which metric to prioritize
    3. It shows exactly where the model's output differs from expected, helping diagnose what went wrong
    4. It generates new test cases based on the differences

13. What would be a potential problem if an eval suite only contained test cases that the model already passed?

    1. The eval would run too slowly
    2. The model would become overconfident
    3. The eval would not catch regressions because there's no signal for when performance gets worse
    4. The test cases would corrupt the model

14. The lesson mentions that AI cannot capture quality dimensions you never measured. What does this mean in practice?

    1. If you don't write a test for a specific quality (like politeness or creativity), the eval won't detect problems in that dimension
    2. AI will always measure everything regardless of what you test
    3. Quality dimensions are not important for eval
    4. The model will automatically improve any quality you don't test

15. Why should eval reports be written to a persistent format rather than just displayed on screen?

    1. Screen displays use more memory than files
    2. Written reports can be stored in version control, compared over time, and referenced when investigating issues
    3. It is required by computer privacy laws
    4. Written reports make the model run faster