Summary: At first glance, a simple error message may not seem worth a second look—but when misinterpreted expectations meet rigid digital systems, the outcome becomes a masterclass in miscommunication and user experience breakdown. Today we dissect what happens when a misunderstanding of a data format leads to an unhelpful response, and what content marketers, UX designers, and support teams must extract from these moments if they ever want to build trust at scale.
What Looks Like Gibberish to You Might Be a Map for Someone Else
The message in question read as follows: “I apologize, but the text you provided does not appear to be a website text with a main story that needs to be extracted and rewritten. The text you provided seems to be a JSON response indicating an error message related to insufficient account balance.” That’s not a narrative. That’s a diagnostic from a machine expected to think like a human. Or maybe the opposite.
Let’s unpack this without blaming the algorithm or the user. On one side, someone hoped to extract a meaningful story from a snippet of digital information. On the other, the system did what it was designed to do—it checked for a linear story in plain text, didn’t find one, and defaulted to a safety message. The problem? Both parties were acting rationally, but built on wildly different assumptions.
Misaligned Assumptions Break Trust Before Service Ever Begins
No one sets out to confuse users. But whenever expectations aren’t met, people feel confused, rejected, or ignored. It’s not about code versus copy—it’s about outcomes versus assumptions.
Imagine you ask a tailor to make you a custom suit, and they respond, “This fabric isn’t cloth. It’s a return-exchange slip.” They’re not wrong. But you’ll walk away annoyed, not informed. That’s exactly what happens in most digital conversations where structured data hits a natural language endpoint: you don’t get anything wrong—you just don’t get what you expected. That’s where disappointment lives. And disappointment kills engagement in content marketing faster than bad grammar.
What Should Have Happened Instead?
Instead of a canned refusal, a better system would’ve said, “It looks like this is a system message rather than a story. Are you trying to debug something? Or rewrite this into a customer-friendly message?” That’s what Chris Voss calls calibrated questions—open-ended prompts that keep the conversation alive instead of shutting it down.
Mirroring would’ve helped too. Repeating back: “a JSON response indicating insufficient account balance?” gives the user a feeling of being heard. Even better, shaping the tone with a bit of empathy—“Looks like you’re dealing with a system error. Do you want to translate this into something people can actually understand?”—would’ve kept momentum instead of forcing a restart.
This Happens Because Machines Don’t Yet “Get” Context
Here’s the hard truth: structured data like JSON isn’t inherently dumb, and human readers aren’t inherently wrong. They just speak different languages. One is built for machines to interpret, not people. When people feed machine logic into a human-style storytelling prompt, they’re already halfway disappointed by what they’ll get back.
But that failure to bridge intent isn’t just a UX concern—it’s a marketing issue. Every unmet inquiry is a lost conversion. Every unresolved friction point is anti-content. Every misunderstood question is a missed opportunity to become the user’s hero at their moment of struggle. That’s on us. Not on them.
Stop Punishing the Wrong Questions—Redirect Them
It’s not your audience’s job to format their input perfectly. If someone hands you a busted JSON error and asks for help transforming it, they’re doing what every good customer does: reaching out with the problem they’ve got, not the problem you wish they’d have.
This is the moment to earn trust. You justify their confusion by showing it’s a common stumbling block. You confirm their suspicion that raw machine codes don’t talk like people. You allay their fear that they messed up—by showing you’ve seen worse.
That’s textbook Blair Warren persuasion: confirm suspicions, justify failures, encourage dreams. The dream here is clean, clear, compassionate communication. The failure? Misunderstood input. And the suspicion? Probably that people think they’re dumb for submitting something a computer didn’t like. If you kill their momentum now, they don’t come back.
Don’t Teach the User—Train the Interface
Let’s be blunt. Telling users they fed a machine the wrong format is laziness disguised as error handing. Fix the interface, not the user. Build decision trees that recognize system messages masquerading as requests. Prompt with strategic curiosity: “Looks like code—what are you trying to do with it?” That keeps the bridge between what they gave you and what they want from you intact.
Remember, persuasion starts after the first “no.” Your users aren’t failing—they’re telling you where things break. Use their confusion as feedback. Shape your funnels and bots to detect misunderstandings. And when they feed you digital spaghetti, smile and ask: “Are you trying to turn this into dinner?”
The Takeaway: Communication is What’s Understood, Not What’s Sent
The trap here isn’t a bad reply. It’s how easy it is to write off the miscommunication as the user’s fault and move on. But every moment like this leaves a footprint. Not just in broken interactions, but in disengaged users who leave systems feeling more lost than helped.
If you’re serious about marketing—especially technical services or software—build experiences that don’t shame the learning curve. Build empathy into your logic trees. Ask better questions. Stay curious enough to keep the conversation open.
Because when someone submits code where you expected a story, they’re not giving you bad input. They’re giving you a chance to do better marketing.
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Featured Image courtesy of Unsplash and Brett Jordan (gz-fIvZEgKQ)
