Q1/10
What is the fundamental mechanism behind how large language models work?
Q2/10
In Google's T-C-R-E-I framework, which component provides the model with examples of what a good result looks like?
Q3/10
Why does Chain of Thought (CoT) typically improve accuracy on reasoning tasks?
Q4/10
What is the key difference between Tree of Thoughts (ToT) and standard Chain of Thought (CoT)?
Q5/10
What is the "curse of knowledge" in the context of prompt engineering?
Q6/10
What is the main goal of the "interview technique" when working on a complex prompt?
Q7/10
In the ReAct (Reason + Action) cycle, which element is responsible for analyzing the result returned by an external tool?
Q8/10
What is the advantage of a multi-agent system over a single massive prompt that describes all tasks?
Q9/10
What is the purpose of a Prompt Router in enterprise AI architecture?
Q10/10
What role does a powerful "teacher model" play in synthetic data generation?