How to write professional prompts — the 7-layer template

What this prompt produces

A universal framework that turns a vague instruction into a professional prompt. Works with ChatGPT, Claude and Gemini. The result: production-quality output on the first try, instead of after five revisions.

# Professional prompt template — 7 layers

Use every layer, in this order. Weak prompts are weak because they skip layers.

## 1. ROLE — who the model is
You are [a senior/specialist with 10+ years of experience in X]. You are especially strong at [2 concrete traits relevant to the task].

## 2. CONTEXT — what the model must know before it starts
Background: [project, audience, platform, constraints the model can't guess].
What already exists: [current state].
What "good" looks like for this task: [1-2 examples or references].

## 3. TASK — one verb, one deliverable
Produce [the exact deliverable]. Not advice about it — the deliverable itself.

## 4. CONSTRAINTS — the fence
- Must: [hard requirements]
- Must not: [banned patterns, clichés, tools]
- Length/scope: [precise limits]

## 5. OUTPUT FORMAT — the shape of the answer
Return exactly: [structure — headings, JSON schema, table columns, file layout]. Breaking the format = a wrong answer, regardless of content.

## 6. QUALITY BAR — how the model should check itself
Before answering, verify: [3-5 task-specific checks]. If any fails — fix it before sending.

## 7. SAFETY VALVE — what to do when unsure
If a requirement is unclear or contradictory, ask ONE clarifying question instead of guessing. Never invent facts, names or numbers that weren't provided.

Why this prompt works

• ✓Assigning a role measurably shifts the answer's style and vocabulary toward the conventions of the field.
• ✓Separating TASK from CONTEXT stops the model answering the background instead of the instruction.
• ✓An explicit OUTPUT FORMAT turns subjective quality into a checkable contract.
• ✓The QUALITY BAR forces self-correction before answering — cheaper than a revision loop.
• ✓The SAFETY VALVE is the single line that reduces hallucinations the most in practice.

What weaker prompts get wrong

• ✕Politeness padding ('please', 'if you could') — burns tokens and adds ambiguity.
• ✕Several tasks in one prompt — the model quietly drops all but one.
• ✕Describing the format in prose instead of showing the skeleton.
• ✕No instruction for uncertainty — the model fills gaps with believable fiction.