The 5-Part Prompt Framework — ChatGPT
Every strong prompt combines five parts: Role (who the AI should act as), Context (background info it needs), Task (the specific ask), Format (how you want the output structured), and Constraints (length, tone, things to avoid).
The prompt
You are a senior email marketer. Context: I run a 3-person SaaS startup selling project management software to freelancers. Task: Write a re-engagement email for users who signed up but never logged in. Format: Subject line + 120-word body, 1 CTA button text. Constraints: friendly tone, no corporate jargon, no exclamation marks.
Pro tip: If an answer feels generic, you almost always forgot the "Context" or "Constraints" part — add them back in.
How to use this prompt
- Copy the prompt using the button above.
- Add any context specific to your situation — the more specific you are, the better the output.
- Paste it into ChatGPT and run it.
- If the answer feels generic, add constraints: audience, length, tone, and what to avoid. That single change fixes most weak output.
Learn the technique
The 5-Part Prompt Framework
Module 1 — Anatomy of a Prompt · Prompt Engineering Fundamentals
Related prompts
The 5-Part Prompt Framework — Claude
Every strong prompt combines five parts: Role (who the AI should act as), Context (background info it needs), Task (the specific ask), Format (how you want the output structured), and Constraints (length, tone, things to avoid).
ChatGPTZero-Shot vs Few-Shot Prompting — ChatGPT
Zero-shot means asking directly with no examples.
GrokZero-Shot vs Few-Shot Prompting — Grok
Zero-shot means asking directly with no examples.
ChatGPTChain-of-Thought Prompting — ChatGPT
Asking a model to "think step by step" before answering measurably improves accuracy on reasoning, math, and multi-step logic tasks because it forces the model to externalize intermediate steps instead of jumping to a guess..
ClaudeChain-of-Thought Prompting — Claude
Asking a model to "think step by step" before answering measurably improves accuracy on reasoning, math, and multi-step logic tasks because it forces the model to externalize intermediate steps instead of jumping to a guess..
ChatGPTStructured Output (JSON & Tables) — ChatGPT
When you need output your app or spreadsheet can actually use, explicitly request a schema.
Related across the site
10 Prompt Mistakes That Make AI Output Generic
The specific, fixable reasons AI output comes back bland — and what to write instead. Each mistake includes a before-and-after example you can apply immediately.
Prompt Engineering Fundamentals
Learn the building blocks of a great prompt — role, context, task, format, and constraints — and how ChatGPT, Claude, Perplexity, and Grok each respond differently to the same instructions.
Chain-of-Thought Prompting — ChatGPT
Asking a model to "think step by step" before answering measurably improves accuracy on reasoning, math, and multi-step logic tasks because it forces the model to externalize intermediate steps instead of jumping to a guess..
ChatGPT vs Claude vs Perplexity vs Grok — Grok
The same task often needs a slightly different prompt style per model.
ChatGPT vs Claude vs Perplexity vs Grok — Perplexity
The same task often needs a slightly different prompt style per model.
Context Engineering vs Prompt Engineering: What Actually Changed
Context engineering is the term that replaced prompt engineering in serious AI work. It is not a rebrand — the job genuinely shifted from writing better instructions to controlling what the model can see. Here is the difference and why it matters.