Refers to the variation in how different people interact with artificial intelligence systems. Even when people use the same AI tool, their approaches, expectations, and interpretations may differ significantly. This variability influences how effective the AI is in practice and can affect whether systems succeed, stall, or produce inconsistent results in real-world settings. For example, users may:

• Ask questions in different ways.
• Use different levels of detail when providing instructions or context.
• Have different expectations of what AI systems can do.
• Interpret AI responses differently.
• Incorporate AI outputs into their work in different ways.

Because of this variation, the same AI system can produce very different results depending on the user.

In early testing and research on AI systems, differences in how people used these tools were sometimes treated as “noise” in the data—something to average out in order to measure system performance. More recent research suggests this variability is an important signal. Studying how people actually use AI can reveal why systems perform well for some users but not others, and why certain deployments succeed while others encounter unexpected risks.

Several related practices and terms include:

• Prompt engineering focuses on designing prompts—questions, instructions, templates, or workflows—that guide AI systems toward more reliable outputs.
• Prompt templates structure how users interact with AI systems.
• Prompt chaining sequences multiple prompts to generate more complex results, improved human–AI interface design, and efforts to strengthen AI literacy so users better understand how to interact effectively with AI tools.