LLM Evals & Observability · Basics
LLM Evals & Observability — Basics
20 practice questions on LLM Evals & Observability. Every question is written from a specific moment in a real lecture, and after you answer it links to that exact timestamp so you can check it yourself.
What this pack asks
The free questions in this pack. Answer choices and explanations appear as you play.
Large language models trained mainly for tasks like summarization often turn out to handle arithmetic or commonsense reasoning too. What single mechanism underlies all of this behavior?
Answer this one →In an evaluation pipeline that pairs each input with a ground-truth output, what does the "LLM as a judge" approach mean?
Answer this one →When building applications on top of a hosted large language model via API, why does the traditional train/validation/test evaluation workflow break down?
Answer this one →
Written from lectures by DeepLearning.AI, Krish Naik. Not affiliated with or endorsed by any university, channel, creator or certification program.