Generative AI & LLMs
LLM Evaluation & Testing
8 practice questions. Free questions open a full answer guide; the rest unlock with Pro.
- For a retrieval-augmented (RAG) feature, how would you measure whether the model's answers actually stay grounded in the retrieved documents rather than making things up?
- How would you build an offline evaluation for an LLM feature so that you can catch regressions before they ship when you change the prompt, model, or retriever?Go Pro
- A team is shipping an LLM-powered feature and the only evaluation so far is engineers reading a handful of outputs before each release. How would you build an evaluation harness for an LLM feature that you'd actually trust to gate a release?Go Pro
- You're using an LLM as the judge to score another model's outputs at scale. How do you make sure the judge's scores are trustworthy?Go Pro
- When evaluating an LLM feature's output quality, how do you decide between reference-based metrics, an LLM-as-judge, and human review? When does each make sense?Go Pro
- What is the "LLM-as-a-judge" approach to evaluating an LLM feature, and when is it a reasonable thing to do?Go Pro
- Your team uses a strong model as an LLM-as-a-judge to score outputs in your evaluation pipeline. What are the failure modes of that approach, and how do you make the judge's scores trustworthy enough to act on?Go Pro
- You changed the prompt for a production LLM feature and want to be confident you didn't make its answers worse. How would you test that before shipping?Go Pro
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