A credit-union wire-transfer tester gets cleaner case drafts after switching to a model further trained on prompt–expected-response pairs so it obeys “list three boundary cases.” Which LLM category is that?
Select an answer to reveal the explanation.
Short Explanation
First you build a general student, then you coach them to follow directions. Instruction-tuned LLMs start from foundation models and are further aligned on prompt–response pairs. That is why “list three boundary cases” lands more cleanly.
Full Explanation
Instruction-tuned models are foundation LLMs later trained to follow human instructions using prompt and expected-response style data. Testers notice better obedience to explicit drafting instructions such as listing boundary cases. That differs from an unadapted foundation base, from symbolic rule engines, and from reasoning models specialized for heavier multi-step inference. Keeping the categories distinct is the Chapter 1 GenAI-1.1.3 skill.