AI Tools: Langfuse Trace-Linked Evals

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-tools-ai-langfuse-trace-eval-r10a4-creators

1. What is the primary benefit of linking traces to eval scores in a production AI monitoring system?

   1. To generate more prompt variations
   2. To automatically retrain the model with new data
   3. To reduce the cost of API calls
   4. To surface regressions before users complain

2. In the context of production AI monitoring, what does it mean to 'sample 1% of traces'?

   1. Running evals on 1% of available model variants
   2. Evaluating a representative subset of production traces
   3. Testing prompts on 1% of user base
   4. Using 1% of compute budget for evaluation

3. What is an LLM-as-judge evaluation?

   1. A benchmark dataset of known correct answers
   2. Using one LLM to evaluate outputs from another LLM
   3. A human expert rating model responses
   4. Using a rule-based script to check output format

4. Why should evaluation rubrics be re-anchored quarterly according to the material?

   1. Because LLM judges change with model updates
   2. To reduce the number of test cases
   3. To comply with data privacy regulations
   4. To increase scoring speed

5. What is a key limitation of LLM-as-judge evaluations?

   1. They are too expensive to run at scale
   2. They require labeled training data
   3. They cannot be automated
   4. They change with model updates

6. What does 'tracing' refer to in the Langfuse context?

   1. Drawing neural network architecture diagrams
   2. Logging API request headers
   3. Linking each prompt to its completion and tool calls
   4. Visualizing user interface flows

7. What does the rolling pass rate refer to in production evaluation?

   1. The speed at which tests complete
   2. The percentage of recent traces meeting quality thresholds
   3. The percentage of users who pass a tutorial
   4. The rate of model parameter updates

8. What should happen when eval scores drop in production?

   1. API responses should be slowed down
   2. The team should be alerted to investigate
   3. Users should be notified immediately
   4. The system should automatically fine-tune the model

9. Which of the following is identified as something AI cannot do in this evaluation workflow?

   1. Generate test cases
   2. Score outputs automatically
   3. Replace human review
   4. Detect regressions instantly

10. What is an 'observability blind spot' in AI production systems?

    1. A metric or behavior that isn't being tracked
    2. A slow database query
    3. A period when the server is down
    4. An error message users see

11. What is the relationship between tracing and eval scores in Langfuse?

    1. Traces replace the need for evals
    2. Evals are run separately from traces
    3. Every trace is linked to an eval score
    4. Traces only log errors, not scores

12. Why is sampling traces rather than evaluating all traces important?

    1. It prevents model drift
    2. It ensures every user is tested
    3. It eliminates the need for judges
    4. It's more cost-effective at scale

13. What does it mean to 'score with a stronger judge'?

    1. Use a more expensive API tier
    2. Use a larger or more capable model for evaluation
    3. Apply stricter rubrics manually
    4. Prioritize high-priority users

14. What happens if you create bad evals?

    1. The system automatically fixes them
    2. The model ignores them
    3. They cannot be fixed by the AI alone
    4. They improve automatically over time

15. What is the purpose of a regression in production AI systems?

    1. A new feature launch
    2. A previously working feature that stopped working well
    3. A performance speedup
    4. A cost reduction