Evals: How You Actually Know if Your AI Feature Works

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-ai-foundations-evals-final1-creators

1. What is the primary function of evals in AI feature development?

   1. They automatically test AI outputs against predefined expected behaviors
   2. They select which model to use in production
   3. They replace all human review of AI outputs
   4. They generate new training data for fine-tuning models

2. A team notices their AI feature started producing worse outputs after changing the system prompt. What role did evals play?

   1. They trained the model to fix the issue
   2. They selected an alternative model automatically
   3. They provided new training examples
   4. They served as regression tests catching the performance drop

3. Which metric would an eval most likely measure that generic benchmarks typically miss?

   1. Mathematical problem-solving accuracy
   2. User-impacting metrics specific to the product
   3. Common sense reasoning rankings
   4. General language understanding scores

4. Why might using one LLM to evaluate another LLM's outputs introduce bias?

   1. The judge model was trained on smaller data
   2. The judging LLM has its own preferences for length, style, and agreeableness
   3. The judge always produces random outputs
   4. The judge cannot understand the task domain

5. What should a team do when using LLM-as-judge to avoid optimizing for what the judge prefers rather than what users need?

   1. Remove all length preferences from the judge
   2. Use multiple judges with different biases
   3. Spot-check judge outputs against human review
   4. Disable the judge after first use

6. What is a key limitation of relying entirely on automated evals for subjective outputs?

   1. Evals are too expensive to run at scale
   2. Evals cannot fully replace human review for subjective content
   3. Evals require too much manual setup
   4. Evals cannot detect factual errors

7. When comparing two different model versions for the same feature, what advantage do evals provide over just observing user feedback?

   1. Better marketing materials
   2. Lower computational costs
   3. Faster deployment timeline
   4. Objective comparison with consistent test cases

8. How many input/expected-behavior pairs does the lesson recommend for a core feature eval?

   1. Twenty
   2. Fifty
   3. Five
   4. One hundred

9. What is the relationship between evals and building intuition about system failures?

   1. Evals make failure patterns random
   2. Running evals over time reveals patterns in where the system fails
   3. Intuition is only built through user reports
   4. Evals eliminate the need for intuition entirely

10. A team wants to evaluate a fine-tuned model against their original model. What is the most objective way to do this?

    1. Ask users which model they prefer
    2. Count the number of parameters
    3. Compare the model sizes
    4. Run both models against the same eval test set

11. What happens if a team runs evals only once and never again after deploying their AI feature?

    1. The evals will continue catching all future issues automatically
    2. They may miss regressions when prompts or models are later updated
    3. The feature will become more reliable over time
    4. They will never need to update their prompts

12. Why do teams without evals operate in a 'vibes-driven' manner?

    1. They rely on subjective feelings about performance rather than measurable data
    2. They avoid using AI entirely
    3. They focus only on user interface design
    4. They use only visual interfaces

13. Which statement about AI capabilities in evals is correct?

    1. AI can replace all human involvement in eval design
    2. AI can create perfect evals on the first try
    3. AI can fully automate eval creation in one pass
    4. AI can help create and run evals but cannot eliminate the need to update them as products evolve

14. What type of inputs should a comprehensive eval set cover?

    1. Important use cases and known failure modes
    2. Only edge cases that rarely occur
    3. Random inputs chosen by the model
    4. Only the most common user queries

15. What does the lesson compare evals to in software development?

    1. Production deployment pipelines
    2. Unit tests that run automatically against code changes
    3. User acceptance testing only
    4. Code review processes