The CAIPM framework highlights that effective AI adoption depends not only on tool availability but also on user interaction behaviors that improve output quality over time. In this scenario, the key issue is that users accept the first response without refinement, leading to suboptimal outcomes.

The requirement is to improve output quality through natural interaction , without relying on structured templates or heavy training. This directly points to the practice of iteration , where users refine prompts, ask follow-up questions, and progressively improve results through dialogue with the AI system.

Iteration is fundamental to generative AI usage because initial outputs are often drafts rather than final answers. By encouraging users to clarify, expand, or adjust their requests, organizations enable continuous improvement in responses without requiring complex prompt engineering knowledge.

Other options are less aligned with the goal:

Being specific improves prompt quality but still relies on upfront precision rather than ongoing refinement.

Setting the role is a useful technique but requires more structured prompting knowledge.

Providing templates contradicts the requirement to avoid complex predefined structures.

CAIPM emphasizes that organizations should promote conversational, iterative engagement as a low-friction way to enhance AI output quality and build user confidence.

Therefore, the correct answer is Iterate , as it best supports continuous improvement through natural interaction.