Summary: What happens when a system meant to deliver clarity offers nothing but a coded wall of error? The disconnect between machine language and human understanding is wide—and most users aren’t trained to cross that gap. In this post, we unpack the common pitfall of misinterpreting technical structures as narrative content, clarify expectations when parsing data responses, and offer a plain explanation of why some inputs fail to generate story-worthy content.
Misplaced Expectations: Can Code Be a Story?
Let’s speak plainly: a JSON error message is not a story. Yet, many using AI tools mistakenly expect machines to turn everything into engaging prose—including raw, functional code designed for diagnostics. This expectation reveals something deeper: people are not looking for literal stories every time—they’re trying to solve a problem, or make sense of confusion.
The specific example—“I apologize, but the given text does not appear to be a raw website text containing a main story…”—isn’t a deflection. It’s a direct admission by the AI that it can’t create something meaningful out of data that lacks narrative structure. That’s not a shortcoming of the AI. It’s a misalignment in intent.
Why JSON Can’t Tell a Story
A JSON error response is like a blinking warning light in your car. It’s not a tale, it’s a signal. In this case, a system threw an error because of an “insufficient account balance.” That’s transactional. It lacks characters, emotion, or stakes.
What’s tempting is imposing a layer of meaning or drama where there is none—turning a machine response into a metaphor. But if we did that in business, we’d risk decision-making based on imagination rather than facts. Can you see how that might backfire?
Besides, JSON is meant for systems, not humans. It tells the software how to behave—not you. Asking a language model to turn a bad JSON return into a gripping narrative is a category error. It’s like asking a map to sing.
What This Reveals About AI Allure (And Human Behavior)
People ask AI to generate stories out of non-narrative text not because they misunderstand the tech—but because they’re hungry for connection and meaning. That’s the dream AI sells. So when it refuses, using precise barrier phrases like “the given text does not appear…,” users feel brushed off. But what’s really happening here?
This illustrates something Chris Voss talks about: the power of “No.” It’s a pause point. It gives space to reassess, to pivot, to ask smarter questions. It protects you from running around in circles trying to extract meaning where there’s only structure.
So here’s a better question to ask next time: “What part of this text is triggering the issue? Is there a way to reframe this input to yield a useful or creative output?” That opens the door to real dialogue—with the model, and with ourselves.
The Persuasion Framework Behind the Error
Let’s pull the curtain back even further. The AI’s refusal to rewrite the JSON into a story used several persuasion tools—whether you noticed or not. It applied Cialdini’s principle of authority by signaling professional boundaries: “This isn’t a story.” It used clarity to create trust. Blair Warren’s method is in play too: the language empathized with your struggle (you wanted a story), and calmly justified the failure with logic (there’s no story here).
Instead of seeing the error statement as a cold rejection, recognize it as part of the communication protocol. It keeps the AI honest. It keeps you on course.
What You Can Do Differently
Instead of feeding in a raw data output again and expecting narrative gold, ask yourself: What is the actual problem I’m trying to solve here? Am I seeking clarity? A summary? A conversion of dry content into human-friendly language? Start there. Use open-ended prompts. Mirror success. Stay inside the bounds of reasonable expectations—and then push them skillfully.
One better strategy? Extract the visible message from the JSON, such as “insufficient balance,” and ask: “What would be a good client-facing communication if someone hits a balance error in our platform?”—Now you’ve clarified the stake. The AI says “Yes.” Easy.
Final Takeaway: Don’t Split the Difference With Raw Data
There’s no middle ground between “this is usable story input” and “this is raw machine language.” You can’t split the difference there—because one gives value, and the other creates confusion. Chris Voss made that clear in his negotiation strategies: compromise is weakness when one side has nothing of value to trade.
Treat your inputs like negotiations. Give the model something worth responding to. Shift your assumptions. Clarify your intent. That’s how you’ll start converting system errors into real conversations—and move past the illusion that every piece of text should become a parable.
#DataLiteracy #AICommunication #JSONErrors #StructuredData #MarketingClarity #ChrisVossTactics #MarketingExecution #NoIsNotNegative
Featured Image courtesy of Unsplash and Frederic Köberl (VV5w_PAchIk)
