An interlibrary-loan tester pastes raw user stories into a foundation model and receives long essays instead of given-when-then cases. What best explains this outcome for test artefact generation?
Select an answer to reveal the explanation.
Short Explanation
A foundation model is like a talented writer who never saw your house style guide—creative, but not locked to given-when-then. For a constrained test format, you usually add adaptation or reach for an instruction-tuned model that expects those instructions. Flexibility alone does not guarantee the artefact shape you need.
Full Explanation
Foundation models are broadly capable next-token predictors without a strong bias toward a particular test template. Instruction-tuned models (or further adaptation such as fine-tuning or careful prompting wrappers) steer outputs toward formats testers require. Observing free-form essays after dumping raw stories is expected without that adaptation, not proof that GenAI cannot help with testing.