Stop Calling AI an Intelligent Intern: Why Language Matterse
In the evolving conversation around generative AI, language is not just descriptive - it’s directive. The metaphors we use to explain large language models (LLMs) quietly shape user behavior, expectations, and, ultimately, outcomes. One increasingly popular analogy is to refer to ChatGPT or similar tools as “intelligent interns.” On the surface, it is a friendly and accessible metaphor, but beneath it lies a fundamental misunderstanding of how these systems function. Interns possess self-awareness, the ability to learn through experience, and often a sense of responsibility. LLMs do not. They do not reason. They do not understand. And perhaps most critically, they do not know when they are wrong.
In my own research and professional work, I have found that a more accurate framing is to think of LLMs as 13-year-olds who hallucinate. They are fast, fluent, and eager, but they tend to make things up without structure, oversight, or clear instructions. That is not a bug but a known behavior of how these models generate language. This isn’t about being alarmist; it is about being honest. Framing AI in a way that reinforces caution, structure, and responsibility does not diminish its potential; it unlocks it safely.
That is why I reference “CHATS,” a memorable and flexible acronym that guides how we prompt LLMs and how we govern our interactions with them. At the prompt level, CHATS stands for Context, History, Audience, Tone, and Sources. These elements improve prompt clarity and reduce hallucination, particularly in business settings where consistency and communication matter. But CHATS also applies at a broader level, reminding users of the guardrails necessary for safe AI use: the need for structured Context, organizational and model History, Audience-appropriate messaging, responsible Tone, and most importantly, verifiable Sources.
It is the last element, Sources, that too often gets overlooked in casual use. An LLM’s ability to sound confident while being wrong is one of its most significant risks. Users unfamiliar with the limits of AI may assume that fluent output equals factual accuracy, especially when the model is framed as “intelligent.” That assumption breaks down quickly when models generate fictitious citations, misquote data, or hallucinate industry standards. By making “Ask for sources and verify them” a default behavior, we improve outcomes and safeguard trust.
Cognitive and behavioral research supports this. The metaphors we use do not just explain; they influence. Calling an LLM an intern invites delegation. It implies that the system will get better over time, that it can learn, and that it bears some degree of responsibility for its performance. But, LLMs do not learn dynamically in conversation. They do not retain context between sessions, and they do not improve unless the underlying model is retrained. Calling them “intelligent interns” may feel modern and empowering, but it encourages users to abdicate critical thinking. By contrast, referring to them as “hallucinating 7th graders” evokes a sense of vigilance. It primes us to supervise, verify, and take ownership of outcomes.
As AI tools become more deeply embedded into workflows, leadership, and strategic planning, we owe it to ourselves and our organizations, to be precise, not just in how we use them but in how we talk about them. CHATS isn’t just a better prompt; it’s a mindset. One that centers clarity, context, and critical thinking in every interaction. Because when we get the language right, we are far more likely to get the outcomes right, too.
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