Summary: Not every piece of content can—or should—be turned into a story. Sometimes, what you’re given is data. Raw, factual, unvarnished structure like a JSON response from an API. And trying to squeeze poetry or plots out of structured machine logic is not only misguided, it misunderstands both the function of data and the purpose of storytelling. Today, let’s walk through what went wrong in this specific situation and—more importantly—what deeper lessons it reveals about communication, context, and expectations in the world of content strategy and tech integration.
Expecting a Story Where There Is None
The prompt that led to this post was straightforward: someone attempted to feed a JSON error payload to an AI and expected it to create a narrative. But the output response was correct and brutally honest: “Unfortunately, the provided text does not contain a story that can be extracted and rewritten. The text appears to be a JSON response from an API, indicating an error related to insufficient account balance. There is no narrative or story present in this data. I cannot rewrite a story from the given text, as it does not contain one.”
Let that sit for a minute. There’s no story there because there was no intent to communicate a story in the first place. What you had was a machine protocol—software talking to software. The expectation that creativity could be coaxed out of a syntax-driven transaction ignores what each medium is actually designed to do.
The Nature of JSON: Clarity, Not Creativity
JSON—JavaScript Object Notation—is not trying to be emotional. It’s not weaving a plot arc or evoking tension. It’s structured data. Hierarchical. Predictable. Its job is to carry values through systems that demand consistency and syntax above all else. Such a system produces output like:
{
"error": {
"code": 402,
"message": "Insufficient account balance",
"details": "Transaction failed"
}
}
That’s not a story. That’s a transaction report. Trying to narrativize that would be like reading a stop sign and trying to imagine the tragic biography of the steel it’s made from. Compelling, possibly. But also irrelevant to the task at hand.
Why Context Misalignment Matters
What this case highlights is a mismatch between the input format and the desired output behavior. This is something I see every day in marketing and tech consulting. The client brings what they think is raw material for a powerful ad, but it’s sterile—copy-pasted boilerplate, bullet points from a compliance team, or worse, output straight from an analytics platform. Then they’re frustrated when that material doesn’t magically convert into high-performing content.
Why does this happen? Because people underestimate intent. They confuse data with meaning. They confuse facts with context. Expecting emotion and nuance from a system built for precision and uniformity is like fishing in a swimming pool.
Where Value Actually Lives
There’s no shame in misunderstanding this—the modern push toward automation and content-at-scale has made these missteps easy to make. But if you want to use machine-generated data for storytelling—especially in marketing—your job is to translate, not retell. You don’t rewrite the JSON. You interpret its implications.
Let’s take that “Insufficient account balance” message. Where could that actually go if we were crafting a relevant human narrative?
- It could headline a blog post about common causes of API errors in fintech apps.
- It might set the stake for a use case showing how automated alerts improve user retention.
- It might support a product pitch to improve error-handling transparency inside SaaS dashboards.
But those are contextual uses, not rewrites of the raw error message. The story isn’t in the data—it’s in what we choose to say about it, how we explain, apply, or solve the issue it raises. That’s where clients feel seen. That’s where prospects feel understood.
The Teaching Moment for Content Teams
This example is a cautionary tale. It teaches technical teams, copywriters, and marketers a simple rule: form follows function. If you’re given code, don’t expect plot. If you want emotional resonance, provide backstory, intent, conflict—human stuff. Data is the bones, but the story comes from flesh, skin, and motion.
Before you ask a team or a language model to create something persuasive, ask yourself:
- What emotions or decisions are we trying to influence?
- What outcome do we need the reader to reach?
- Is the input suited to that job, or are we forcing it?
If you can’t answer those with clarity, you’re not briefing a content piece—you’re throwing puzzle pieces and hoping they form a painting. That’s not how professionals operate. Structure matters. So does purpose.
Never Split the Medium
Chris Voss’s best negotiation rule also applies here: “Never split the difference.” If the input doesn’t match the expected result, stop the process. Say no. It’s better to pause and reassess than waste time generating content that doesn’t—and can’t—work. Clarity is power. Boundaries are a feature, not a bug.
What’s the right question to ask when you get a data structure as prompt?
“What is this trying to accomplish?” And then mirror the answer. Use structured silence to surface the real objective. Then you can translate. But blindly trying to morph protocol outputs into prose will only confuse everyone involved—including yourself.
Final Word: Value Is in Interpretation
We don’t shine by repeating raw data. We shine by owning our filter—by interpreting noise into signal, and signals into action. That’s the marketer’s job. That’s the technologist’s layer of soft power. Next time you get a 402 error, don’t ask for a fairy tale. Ask, “What does this tell us about the system?” Then tell that story right.
#TechnicalWriting #ContentStrategy #APIErrors #MarketingandData #StorytellingWithData #ClarityOverCreativity #ContentThatWorks
Featured Image courtesy of Unsplash and Stephen Dawson (qwtCeJ5cLYs)
