5 Negative Constraints That Confuse AI (And What to Say Instead)

Why telling an AI what not to do usually makes it do exactly that.

Humans are wired for negation. If I tell my toddler, “Don’t touch the stove,” she (hopefully) understands the concept of avoidance. If I tell my Australian Shepherd, “Don’t eat the router cable,” he looks at me with those chaotic eyes and hears, “Router cable? Interesting. Let’s investigate.”

Turns out, AI models are a lot more like my dog than we’d like to admit.

I see this constantly in my consulting work. A client will come to me, frustrated, saying, “James, I told the bot not to be salesy, and it sounds like a used car salesman from 1998.” Or, “I explicitly said no passive voice, and it’s riddled with it.”

The problem isn’t the AI’s intelligence; it’s the architecture. LLMs are prediction engines. They predict the next token based on the context provided. When you say, “Do not use the word ‘delve’,” you have just injected the token “delve” into the context window. The model sees “delve” and its attention mechanism lights up around that concept. It’s the classic “Don’t think of a pink elephant” paradox.

I learned this the hard way back in my early NLP days, long before ChatGPT was a household name. But recently, with the explosion of “wrapper” apps and custom GPTs, I’m seeing the same mistakes repeated at scale.

So, let’s fix it. Here are the five most common “Negative Constraints” that wreck your prompts, why they fail, and the positive framing (the “Do This”) that actually works.

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