Questions
- Describe three key metrics used in evaluating AI agents.
- What’s the difference between online and offline evaluation?
- What are system-level and application-level evaluations and how do they differ?
- How can LangSmith be used to compare different versions of an LLM application?
- How does chain-of-thought evaluation differ from traditional output evaluation?
- Why is trajectory evaluation important for understanding agent behavior?
- What are the key considerations when evaluating LLM agents for production deployment?
- How can bias be mitigated when using language models as evaluators?
- What role do standardized benchmarks play, and how can we create benchmark datasets for LLM agent evaluation?
- How do you balance automated evaluation metrics with human evaluation in production systems?
