Most people write prompts like they’re ordering coffee. Tell the AI what you want. Hope for the best.
Turns out, that’s exactly wrong.
Not slightly wrong. Structurally wrong. The thing you’re spending the most time on, the task itself, is responsible for less than 3% of output quality. The stuff you’re ignoring is doing all the heavy lifting.
🧮 The Signal Problem
Someone ran 275 production experiments to figure out why some prompts crush it and others flop. The framework borrows from signal processing. Specifically the Nyquist-Shannon theorem: if you undersample a signal, you lose information.
Your prompts are undersampled. Badly.
A prompt has 6 frequency bands. Most people only hit 1 or 2. That’s 6:1 undersampling. You’re leaving most of the signal on the floor before the AI even gets started.
Think about what that means in practice. You spend five minutes crafting the perfect task description, tweaking the wording, rephrasing, second-guessing. Then you hit send and get something generic. The problem wasn’t your task. The problem was everything you didn’t include around it.
Here’s where quality actually comes from across those 6 bands:
- CONSTRAINTS: 42.7% of output quality. What the AI should NOT do matters more than what it should do. This is the single biggest lever in prompt engineering and almost nobody pulls it. Constraints are not limitations. They are signal. “Don’t use passive voice. Don’t summarize at the end. Don’t suggest anything requiring a budget over $500.” Each one filters out a massive surface area of bad output before a single word is generated.
- FORMAT: 26.3%. Output structure, length, tone. Most prompts skip this entirely or add “keep it short” at the end. That’s not a format spec. A real format spec sounds like: “Three paragraphs. No headers. Written at a 9th-grade reading level. Lead with the most important point. End with one concrete action.” Now the AI knows what done looks like.
- PERSONA: 7%. A specific identity, not just “act like an expert.” There’s a massive difference between “be a marketer” and “be a direct response copywriter who spent 8 years writing for subscription businesses and has strong opinions about why most copy fails.” The second one changes the entire frame the AI uses to respond.
- CONTEXT: 6.3%. The situation, the audience, the reason behind the ask. “I’m writing this for a CFO who already understands the technology and is skeptical of vendor claims” gives the AI something to aim at. Without context, it guesses. And it usually guesses wrong.
- DATA: 3.8%. Examples and references the AI needs to actually work from. If you want it to match a tone, show it two sentences in that tone. If you want it to follow a format, give it one example. Examples compress a lot of instruction into something concrete and unambiguous.
- TASK: 2.8%. The actual instruction. The thing 99% of prompts start and end with.
The task is 2.8% of what drives quality. Let that sink in.
⚡ Three Things Worth Stealing Right Now
- Lead with constraints, not the task. “Never use bullet points. Never give generic advice. Never hedge.” That single addition does more than restating your goal five different ways. Most prompts have zero constraints. That’s the problem. Before you write what you want, write what you don’t want. Start with three hard nos. You’ll be surprised how much it changes what comes back.
- Specify FORMAT like you’re briefing a designer. Length, structure, reading level, what “done” looks like. Vague prompts get vague answers. Always. A designer wouldn’t start a project without a brief. The AI shouldn’t either. If you can’t describe what a good output looks like before you send the prompt, you’re not ready to send it yet.
- Make PERSONA specific. “A growth marketer who ran $10M in Meta ad campaigns and hates vanity metrics” works harder than “an expert marketer” every single time. Specificity is the variable. The more precisely you define who is answering, the more precisely you get an answer that matches what that person would actually say. Generic persona, generic output. It’s that direct.
🎯 Try the Tool
The researcher built a free prompt transformer at tokencalc.pro that walks you through all 6 bands. Paste your existing prompt in and it shows you which bands you’re missing and what to add. It’s not a chatbot. It’s a structured checklist that forces you to fill in the gaps you keep skipping.
There’s also a full peer-reviewed paper on Zenodo if you want the methodology behind the numbers. The 275-experiment dataset is real, the percentages are derived from systematic testing, not vibes.
Most people improve their prompts by rewriting the task. After reading this, you’ll improve them by adding what’s around it.
Your next prompt is going to look very different after this.
I built a mathematical framework for prompt engineering based on the Nyquist-Shannon theorem. The #1 finding: CONSTRAINTS carry 42.7% of quality, and most prompts have zero.
by u/Financial_Tailor7944 in PromptEngineering