Your Prompts Are Undersampled.
I Fix That.
I hand-tune each of the 6 specification bands for your exact domain, your exact model, your exact workflow. You get the sinc-formatted prompt, a before/after proof, and an SNR score. The difference is visible in 5 seconds.
See PricingThe Problem You Are Paying Me to Solve
"I think this might be..." "It could potentially..." "There are several options to consider..." This is not the AI being careful. This is specification aliasing. Your prompt is missing the CONSTRAINTS band (42.7% of output quality), so the model defaults to the safest possible output: vague, hedged, and useless.
Every time a human has to fix AI output, you are paying twice: once for the API call, once for the human labor. A sinc-formatted prompt eliminates the rework because the output arrives in the exact format, with the exact level of specificity, following the exact rules your workflow requires.
Raw prompts generate filler: disclaimers, qualifications, unnecessary preamble, trailing summaries. A 200-token answer becomes 800 tokens of fluff. sinc prompts eliminate filler because FORMAT and CONSTRAINTS tell the model exactly what to produce and what to leave out. Less output tokens = lower cost.
You know the output is wrong. You do not know why. The sinc-LLM framework identifies exactly which of the 6 specification bands your prompt is missing and fills them with domain-specific content. That diagnosis is what you are paying for.
Who This Is For
Developers spending $1K+/month on APIs
Your prompts are structurally impoverished. Adding 39 tokens of constraints cuts costs by 97% because the model stops generating filler. The prompt pays for itself in 1 API call.
Teams getting inconsistent output
The model is not unreliable. Your prompt is underspecified. A sinc prompt locks the behavioral boundaries so every run produces the same structure, tone, and specificity level.
Legal, medical, and finance teams
When the AI says "this might be a risk" instead of "clause 4.2 creates a $2.3M liability," someone has to redo the work. Domain-specific CONSTRAINTS eliminate hedging in regulated industries.
AI product builders
Your system prompt is the foundation of your product. A poorly specified system prompt means every user interaction is degraded. I rebuild it from the specification axis up.
Agencies running AI for clients
Every client has different requirements. I build a sinc prompt per client that locks their output quality, format, and compliance rules. You deliver consistent results across your portfolio.
Anyone tired of re-prompting
If you spend more than 10 minutes per day tweaking prompts to get the output you want, a single sinc prompt eliminates that loop permanently. 45 minutes saved daily.
How I Build Your Prompt
Diagnose
I run your current prompt through the sinc-LLM analyzer. I identify which of the 6 bands are missing, which are weak, and which are causing the specific output problems you described. I compute the SNR score of your current prompt.
Research Your Domain
I study your use case, your industry, your compliance requirements, and your ideal output. I identify the domain-specific CONSTRAINTS that your prompt needs. A legal review bot needs different rules than a marketing copywriter.
Build the 6 Bands
I write each band by hand. PERSONA: the exact expert role. CONTEXT: the situation your model operates in. DATA: the specific inputs. CONSTRAINTS: 5 to 15 MUST/NEVER/ALWAYS rules calibrated for your domain. FORMAT: the exact output structure. TASK: the clear imperative.
Test and Measure
I run the sinc prompt on your actual model (ChatGPT, Claude, Gemini, whatever you use). I measure the SNR score, compare the output to your ideal, and iterate until the quality hits EXCELLENT (SNR above 0.80).
Deliver with Proof
You receive: the sinc JSON prompt, a before/after comparison showing your old output next to the new one, the SNR score report, and a brief explanation of what each band does and why. You paste it and see the difference immediately.
What You Get
Pricing
- 1 custom sinc prompt
- All 6 bands hand-tuned
- SNR score report
- Before/after comparison
- Band explanation
- 5 custom sinc prompts
- Full workflow coverage
- Domain-specific constraint library
- System prompt architecture document
- Integration guide
- SNR dashboard for all 5 prompts
- Full LLM pipeline audit
- All prompts decomposed into sinc format
- SNR dashboard across entire pipeline
- Domain constraint library (15+ rules)
- 30-minute strategy call
- Cost reduction estimate with exact numbers
- Priority email support for 30 days
All payments via Stripe. After payment you fill a brief intake form. Performance guarantee: if the sinc prompt does not outperform your original (with evidence), full revision or refund within 7 days.
Performance-Based Guarantee
Every delivery includes a documented before/after comparison with SNR scores. If the sinc prompt does not produce measurably better output than your original, send me both outputs within 7 days and I will either revise the prompt at no charge or issue a full refund. Refund requests require the original output and the sinc output side by side as evidence that no improvement occurred. This protects both of us: you get guaranteed quality, and the work is judged on results, not opinion.
Questions
What do I need to provide?
After payment, you fill a form with: your use case, the LLM you use, your current prompt(s), what is wrong with the output, and what a perfect output looks like. The more detail you give me, the better the result.
How do I use the delivered prompt?
Copy and paste. The sinc JSON works as a system prompt or user prompt with any LLM. No code changes, no integrations, no dependencies. You paste it where your current prompt is and the output changes immediately.
What if I use multiple LLMs?
sinc prompts are model-agnostic. The same prompt works across ChatGPT, Claude, Gemini, Llama, and any other model. If you need model-specific tuning, the Pro package covers that.
How is this different from the free tool?
The free tool on tokencalc.pro uses heuristic templates. It fills missing bands with generic content. My custom service writes domain-specific CONSTRAINTS, calibrated FORMAT specs, and expert PERSONA definitions that the generic tool cannot produce. The difference is like a template resume vs one written by a career coach who knows your industry.
What is the refund policy?
Every delivery comes with a before/after comparison and SNR scores. If the sinc prompt does not outperform your original, send me both outputs within 7 days and I will either revise until it does or refund the full amount. Refund requests require the side-by-side comparison as evidence. This is a performance guarantee, not a try-it-and-return-it policy.
How do I get a strategy call?
The 30-minute strategy call is included with the Enterprise package ($497). Purchase the Enterprise tier and you will receive a calendar link to book your call after submitting your order details.
Built By
Mario Alexandre
I built the sinc-LLM framework in one week working 17 hours a day. It applies the Nyquist-Shannon sampling theorem to LLM prompts. The research paper has 275 production observations, 22 figures, and is published with a permanent DOI. The framework is open source, MIT licensed, and used in production.