You ask your AI assistant to write a simple technical summary. The result is a 500-word meandering prose poem that sounds profound but says absolutely nothing. You sigh, rewrite the prompt, and pray to the tech gods that the next model upgrade will finally fix the “slop” problem.
But here’s the hard truth: the problem isn’t the model. You’re using the wrong tool and blaming the hammer for not being a scalpel.
We don’t need smarter AI; we need an AI vocabulary that makes it impossible for the machine to lie to us.
We’ve poured billions into AI, trying to make it infinitely smart and endlessly creative. But when you’re trying to get reliable, safety-critical output from a chatbot, that infinite creativity is your biggest enemy. You want precision. The AI gives you poetry.
To find the actual cure, we need to stop looking at Silicon Valley and start looking at the aerospace industry. Specifically, the Boeing 737 manuals from 1986.
Back in the 80s, aviation faced the exact same crisis. Mechanics from different countries were reading translated manuals, and ambiguity meant people would fall out of the sky. They couldn’t just “hope” the translations got better. So, they invented Simplified Technical English (STE).
STE stripped the aviation vocabulary down to roughly 1,000 approved words. “Fluid” only ever means a liquid. “Leak” only ever means a fluid leak. No synonyms. No metaphors. No flair.
Generative fluidity is the feature, but it’s also the bug.
When ChatGPT writes a maintenance report today, it might say, “The valve exhibited seepage under stress.” Sounds professional, right? But what does it mean? Is it leaking? Did it fail a test? In STE, the AI would be forced to say: “The valve leaks.” Period. No room for interpretation.
We’ve been blaming AI slop on not having enough parameters, not enough training data, not enough context windows. But the real bottleneck is a communication standards problem. We let the AI speak an unrestricted, chaotic human language, and then we’re shocked when it acts like a human—exaggerating, being vague, and getting creative with the facts.
The fix isn’t to wait for a trillion-parameter model that can read your mind. The fix is to constrain the AI. Force it to output a Domain Specific Language (DSL). Give it a strict, restricted syntax where it has no choice but to tell the exact truth.
Safety isn’t calculated. Safety is enforced.
If you build, use, or manage AI systems, stop waiting for the next upgrade. Look at the 1986 aircraft manual. Cut the vocabulary. Kill the flair. Enforce precision.
If you don’t constrain your AI, it will happily crash your operations with its own beautiful nonsense. Stop blaming the model. Start blaming the lack of constraints.
FAQ
Q: Doesn't restricting the AI's vocabulary kill its usefulness?
A: No, it kills its hallucinations. If you need poetry, let it run free. If you need safety-critical instructions or structured data, lock it down. Context is everything.
Q: What's the practical takeaway for AI builders?
A: Stop feeding the model more data and start building strictly defined output grammars for your specific domain. Make the AI act as a translator into a highly constrained Domain Specific Language (DSL).
Q: Are you saying making AI dumber is the best way to make it reliable?
A: Exactly. We spent a hundred billion dollars making AI infinitely smart, but the real value comes from forcing it to be incredibly, brutally specific.