A community-solar billing tester is told they must first collect a labeled training set before any AI may draft test cases. What Chapter 1 advantage of generative AI for testers corrects that claim?
Select an answer to reveal the explanation.
Short Explanation
You do not rebuild the oven every time you want toast—you use one that is already heated. Pre-trained GenAI lets testers prompt for drafts without collecting a fresh labeled set first. Risks and review still matter later; the Chapter 1 point is the missing extra training phase.
Full Explanation
A practical tester benefit of generative AI is that models arrive pre-trained, so drafting cases or similar artefacts can start from prompts rather than from a new classical-style training job. That does not erase review obligations or later-chapter risks such as hallucinated detail. Classical ML typically needs prepared data, features, and training before it is useful for a prediction task. The solar-billing claim confuses GenAI’s pre-trained use with classical ML’s required training step.