You Have Been Using AI Wrong. Here Is the Proof.

The Uncomfortable Truth

Every prompt you have ever written to ChatGPT, Claude, Gemini, or any other LLM is broken. Not a little bit off. Not almost good. Broken. Here is the proof: your prompts have a signal-to-noise ratio of 0.003. That means for every 1 useful word you give the model, there are 333 words of guesswork it has to fill in on its own.

You type "Summarize this document." Then you complain the summary is too long, too vague, or misses the point. You type "Write me a blog post." You get a boring wall of words full of maybes and warnings. You type "Analyze this data." You get a shallow overview instead of the deep breakdown you wanted. Then you blame the model.

The model is not the problem. Your prompt is the problem.

One Sample of a Six-Band Signal

I am Mario Alexandre. I am an electrical engineer. When I looked at how people talk to LLMs, I did not see a writing problem. I saw a signal problem. And signal science has a rule for exactly this.

The Nyquist-Shannon sampling theorem, published in 1949, says this: to rebuild a signal with N frequency bands, you need at least N samples. If you have fewer, you get aliasing. Aliasing means fake signals that look real but are not there.

When you write a prompt, your intent has 6 bands of information:

1. PERSONA, who should answer (what kind of expert, what voice)
2. CONTEXT, the situation and the facts around it
3. DATA, the exact numbers and details to use
4. CONSTRAINTS, the rules and limits, what NOT to do
5. FORMAT, how the answer should look and how long it should be
6. TASK, what you actually want done

When you type "Summarize this document," you give the model 1 band (TASK) out of 6. The model has to fill in the other 5 bands on its own. Every band it fills in by guessing is a chance for a wrong answer, a vague statement, or a result you did not want.

You are not really giving the model instructions. You are giving it a tiny slice of what you want. Then you blame it for getting the rest wrong.

The 42.7% You Are Missing

I looked at 275 real prompt-response pairs. I measured how much each band affects the quality of the answer. The results will surprise you:

Look at that table again. TASK, the part you think IS the prompt, controls only 2.8% of quality. CONSTRAINTS, the part almost no one writes, controls 42.7%. You are spending all your effort on 2.8% while you ignore 42.7%.

This is why prompt writing feels like magic tricks. People keep changing how they word the task. They try new phrases and different orders. But they are only touching 2.8% of the problem. The other 97.2% is missing or out of control. No clever wording fixes a missing CONSTRAINTS section.

What the Model Does with Your Broken Prompt

When you leave out CONSTRAINTS, the model falls back on its defaults:

• Hedging phrases (like "It is important to note that..." or "While there are many perspectives...")
• Disclaimers (like "I am an AI and cannot..." or "Results may vary...")
• A generic structure (intro, body, conclusion) even if that is not what you need
• A medium length, not too short and not too long
• A bland professional tone that sits in the middle of everything it was trained on

Each of those defaults is the model filling a CONSTRAINTS gap. You did not say "no hedging," so it hedges. You did not say "no disclaimers," so it adds them. You did not say "short and direct," so you get medium-length bland text. The model is doing exactly what you asked, which was almost nothing.

When you leave out FORMAT, the model picks the structure it saw most often in training. That is why every ChatGPT answer looks the same: numbered lists, bold headers, and exactly 5 bullet points. That is not ChatGPT's style. That is what happens when you give no style at all.

The Proof: 275 Observations, 97% Cost Reduction

The numbers from my sinc-LLM research are not guesses. I measured them from real systems doing real work:

97% cost reduction. 97% fewer tokens. SNR went from 0.003 to 0.92. These are not hand-picked results. I measured them across 11 agents doing many different tasks over 275 observations.

The 6-Band Decomposition

Here is the fix. Before you send a prompt, break it into 6 bands. My sinc-LLM tool does this for you, but you can also do it by hand in 60 seconds:

1. PERSONA (12.1%): Say who should answer. Not just "Act as an expert." Be specific: "You are a B2B SaaS copywriter who writes short, high-converting cold emails."
2. CONTEXT (9.8%): Describe the situation. Give the company name, industry, and key facts. Tell the model everything it needs to know about the setting.
3. DATA (6.3%): Give the exact inputs. Numbers, references, specific details. If you want analysis, give the model something concrete to analyze.
4. CONSTRAINTS (42.7%): This is the most important band. What must the output NOT do? Word limits. Banned phrases. Required items. Tone rules. Every constraint you add removes one guess the model would have made.
5. FORMAT (26.3%): Tell the model exactly how to structure the output. "Return: subject line, 3 paragraphs, CTA text." Do not say "write it nicely." Give it a skeleton.
6. TASK (2.8%): Say what you want done. Keep it short. The other 5 bands do the heavy lifting.

A Real Example: Before and After

Here is what a properly decomposed prompt looks like in the sinc JSON format:

{
  "formula": "x(t) = Sigma x(nT) * sinc((t - nT) / T)",
  "T": "specification-axis",
  "fragments": [
    {
      "n": 0,
      "t": "PERSONA",
      "x": "You are a direct-response copywriter who writes high-converting cold emails for B2B SaaS companies. You write in short sentences. You never hedge. Every line earns the next line."
    },
    {
      "n": 1,
      "t": "CONTEXT",
      "x": "DLux Digital is launching a new API product called PayFlow for fintech CFOs. Series A company, 50 employees. The product reduces payment reconciliation time from 4 hours to 12 minutes. Price: $99/month. Competitor Stripe Reconcile charges $299/month."
    },
    {
      "n": 2,
      "t": "DATA",
      "x": "Product: PayFlow API. Price: $99/mo. Target: CFOs at fintech companies, 50-200 employees. Key metric: 4 hours to 12 minutes reconciliation. Competitor: Stripe Reconcile at $299/mo. Launch date: April 1, 2026."
    },
    {
      "n": 3,
      "t": "CONSTRAINTS",
      "x": "Maximum 150 words. No jargon. No buzzwords. No 'revolutionary' or 'cutting-edge' or 'leverage'. One CTA only. Do not mention AI or machine learning. Do not use exclamation marks. Subject line must be under 8 words. First sentence must reference a specific pain point the CFO has. Compliance: CAN-SPAM compliant, include unsubscribe mention."
    },
    {
      "n": 4,
      "t": "FORMAT",
      "x": "Return exactly: (1) Subject line (under 8 words). (2) Email body: 3 short paragraphs. (3) CTA button text (under 5 words). (4) Unsubscribe line. No headers, no formatting, just the raw email text ready to paste into an email tool."
    },
    {
      "n": 5,
      "t": "TASK",
      "x": "Write a cold outreach email for the PayFlow API launch targeting fintech CFOs."
    }
  ]
}

Now compare that to what most people send: "Write a cold email for my new API product PayFlow targeting fintech CFOs." That version forces the model to guess the price, the competitor, the word limit, the tone, the structure, the CAN-SPAM rules, and the ban on buzzwords. Every guess is a place where the answer goes wrong.

Stop Blaming the Model

The models are not broken. GPT-4, Claude, Gemini, these are very powerful tools. They respond to exactly what you give them. The problem is that "Write me a marketing email" is not a real instruction. It is a wish. Models answer wishes the way a genie does: technically correct, but not what you actually meant, because you did not say what you meant.

Say what you mean. Break your intent into 6 bands. Put most of your effort into CONSTRAINTS (42.7% of quality) and FORMAT (26.3% of quality). TASK is the easy part. It is the 2.8% you were already getting right.

Try sinc-LLM free to break down any prompt for you. Read the token optimization guide to see what it costs. Explore my open source framework on GitHub. Or read my full paper with all 275 observations. If you run production LLM systems and want help doing this at scale, I can help.

The age of raw prompting is over. The signal era of AI has started.