The Best ChatGPT Prompt Template Based on Signal Processing Research

Why Most Prompt Templates Fail

Search for "ChatGPT prompt template" and you will find hundreds of results. Most have the same problem. They are built on guesswork, not measurement. They tell you to "act as" or "give context" but never say how much context is enough.

My sinc-LLM framework is backed by 275 real observations and the Nyquist-Shannon theorem. It works for ChatGPT, Claude, Gemini, and any other LLM. Writing good prompts is only half the job. The other half is checking whether your AI vendor writes good prompts too. This article gives you the template. The audit at the end gives you the questions to ask the agency running your AI.

The Template

Copy this template. Change it to fit your task.

PERSONA: [Role with specific expertise]
You are a [specific role] with expertise in [specific domain].

CONTEXT: [Situation and background]
[What project/situation this is for]
[What has been tried or decided already]
[Relevant environment or audience details]

DATA: [Specific inputs]
[Actual data, code, documents, or examples the model should use]

CONSTRAINTS: [Rules and boundaries -- allocate ~42% of your prompt here]
- [Specific exclusion: what NOT to do]
- [Measurable limit: word count, format restriction]
- [Required inclusion: what MUST appear]
- [Edge case handling: if X then Y]
- [Accuracy rule: only use provided data]
- [Style rule: tone, voice, jargon policy]
- [Safety rule: compliance, sensitivity]

FORMAT: [Output structure -- allocate ~26% here]
- [Exact structure: headers, sections, bullet points]
- [Length specification]
- [Code format if applicable]

TASK: [One clear instruction]
[What to do -- this is usually just one sentence by now]

Template in Action: 3 Examples

Example 1: Code Review

PERSONA: Senior software engineer, 10 years Python experience
CONTEXT: FastAPI microservice handling payment webhooks, production
DATA: [paste the function to review]
CONSTRAINTS: Focus on security vulnerabilities only. Do not suggest
  style changes. Flag any unvalidated input. Check for SQL injection,
  XSS, SSRF. Max 5 findings, ranked by severity.
FORMAT: Table with columns: Severity | Line | Issue | Fix
TASK: Review this code for security vulnerabilities.

Example 2: Content Writing

PERSONA: B2B SaaS content writer for developer audience
CONTEXT: Blog post for company engineering blog, readers are senior devs
DATA: Topic: "Why we migrated from Redis to DragonflyDB"
CONSTRAINTS: 800-1000 words. No marketing language. Include specific
  metrics (latency, memory, cost). Must mention tradeoffs honestly.
  No "we're excited" or "game-changing." Technical but readable.
FORMAT: Title + intro paragraph + 4 sections with H2 headers + conclusion
TASK: Write the blog post.

Example 3: Data Analysis

PERSONA: Data analyst for e-commerce company
CONTEXT: Monthly business review, comparing Feb vs Jan 2026
DATA: [paste CSV or key metrics]
CONSTRAINTS: Only analyze metrics that changed by more than 10%.
  Do not speculate on causes without data. Round to 1 decimal.
  Include confidence intervals where possible.
FORMAT: Executive summary (3 bullets) + detailed table + recommendations
TASK: Analyze month-over-month changes and identify top 3 action items.

Why This Template Works: The Math

My sinc-LLM research shows that a prompt is like a sampled version of what you actually want to say. The template works because it makes you cover all 6 specification bands. That meets the Nyquist rate, which means the model can rebuild your full intent from those samples.

The token split (42% CONSTRAINTS, 26% FORMAT) comes from the quality weights I measured across 275 observations. It is not a guess. It reflects how much information each band carries.

Auto-Generate from Any Prompt

You do not have to fill the template by hand every time. The sinc-LLM transformer takes any raw prompt and breaks it into the 6 bands for you. It also spots missing bands and suggests what to add.

I put the whole framework open source on GitHub. You can use it with ChatGPT, Claude, or any LLM that takes text prompts.

The Bigger Question

The template has six bands: PERSONA, CONTEXT, DATA, CONSTRAINTS, FORMAT, TASK. Use it for every ChatGPT, Claude, or open-model prompt. Version it. Test it. Deploy it.

Templating your own prompts is the easy part. The hard part is asking your AI vendor if they do the same. Do they have a written process for testing template changes? Can they show you the diff between v1 and v2 of the prompt that talks to your customers? Most cannot.

{
  "formula": "x(t) = Σ x(nT) · sinc((t - nT) / T)",
  "T": "specification-axis",
  "fragments": [
    {
      "n": 0,
      "t": "PERSONA",
      "x": "You are a ChatGPT power user and template designer. You provide precise, evidence-based analysis with exact numbers and no hedging."
    },
    {
      "n": 1,
      "t": "CONTEXT",
      "x": "This analysis is part of a production system where accuracy determines revenue. The sinc-LLM framework identifies 6 specification bands with measured importance weights."
    },
    {
      "n": 2,
      "t": "DATA",
      "x": "Fragment importance: CONSTRAINTS=42.7%, FORMAT=26.3%, PERSONA=7.0%, CONTEXT=6.3%, DATA=3.8%, TASK=2.8%. SNR formula: 0.588 + 0.267 * G(Z1) * H(Z2) * R(Z3) * G(Z4). Production data: 275 observations, 51 agents."
    },
    {
      "n": 3,
      "t": "CONSTRAINTS",
      "x": "State facts directly. Never hedge with 'I think' or 'probably'. Use exact numbers for every claim. Do not suggest generic solutions. Every recommendation must be specific and verifiable. Include at least 3 MUST/NEVER rules specific to this task."
    },
    {
      "n": 4,
      "t": "FORMAT",
      "x": "Lead with the definitive answer. Use structured headers. Tables for comparisons. Numbered lists for sequences. Code blocks for implementations. No trailing summaries."
    },
    {
      "n": 5,
      "t": "TASK",
      "x": "Create a universal 6-band ChatGPT prompt template for business analysis tasks"
    }
  ]
}