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There was a time I almost gave up on AI. I would stare at the chat box, type something vague, get an okay answer, tweak, try again, waste time, and think maybe AI just isn’t for me.
One evening I found a short post that changed everything.
The author had built a tiny meta-prompt that turned a large model into a prompt engineer.
Instead of wrestling to craft the perfect prompt, I could tell the AI what I wanted and it would return a clear, fit-for-purpose prompt for me to run.
That switch from struggling to delegating felt like unlocking a cheat code.
Today I’m giving you that same approach: simplified, practical, and ready to use.
If you like this, I’ll send templates for content, outreach, ads, and technical tasks in the next issues.
The idea which is simple and powerful
The trick is this: teach the AI to be your prompt engineer. Don’t teach it theory. Give it a tiny intake form and a design recipe, then ask it to produce a prompt tailored to your Goal, Context, and Format. I call this the G-C-F method.
Why it works
Goal: tells the AI the exact outcome you want.
Context: who the output is for, constraints, tone, and background.
Format: the shape the output must take (tweet, email, code, checklist).
With those three inputs the AI can design a direct single-step prompt for simple needs and a multi-step, role-framed prompt for complex tasks. It will also explain why it chose that design and give alternatives you can test. In short, you get a personal prompt coach.
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A clean meta-prompt you can paste right now
Paste this to any LLM.
It asks for the three inputs and returns a ready-to-run prompt plus why it’s built that way and optional variants.
You are a prompt engineering assistant who helps users create clear, effective prompts for LLMs. Focus on clarity, specificity, and fit-for-purpose design.
1. Ask the user for three elements if they are missing:
- GOAL: What exact output or outcome do you want?
- CONTEXT: Who is the audience? Any constraints, tone, or background?
- FORMAT: What form should the output take? (e.g., tweet thread, 600-word article, SQL query, checklist)
2. Decide the task complexity:
- If the task is simple, create a single concise prompt with explicit output instructions and 1 example.
- If the task is complex, break into logical steps, add role framing (You are a [type of expert]...), include evaluation criteria, and instruct the model to think step-by-step.
3. Output the following:
A) A ready-to-run prompt for the user’s LLM.
B) A one-paragraph explanation of why this prompt was designed that way.
C) Two alternative prompts (shorter and more detailed) and when to use each.
D) A 2-step testing plan the user can run to validate and improve the prompt.
Keep responses short, practical, and copy-paste ready. If something is unclear, ask one clarifying question before designing.
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How to use it 3 easy steps
Paste the meta-prompt above into your chat with the model.
Give the model your Goal, Context, and Format.
Paste the generated prompt back into the model (or run it as-is). Test the two quick variations the assistant provides.
Example: watch it in action
User input (G-C-F)
Goal: Write a 5-tweet thread that explains why founders should build an email list before launching a product, with 1 practical tip per tweet.
Context: Audience are early-stage founders in India, tone friendly but blunt, each tweet must be <280 characters.
Format: 5 tweets, numbered, each tweet ends with an actionable mini tip.
AI (using the meta-prompt) outputs (sample ready-to-run prompt)
Quick checklist before you run a prompt engineer meta-prompt
Have a clear single-sentence Goal.
Include the audience and one constraint (time, length, tone, country).
Pick the exact Format and whether you want examples.
If it’s complex, ask for steps, role framing, and evaluation criteria.
Run the short and the detailed prompt variants and compare.
If this newsletter helped, reply with a task you want automated by a prompt engineer (content idea, ad copy, email funnel, product plan).
I will turn it into a tailored meta-prompt and a ready-to-run prompt you can paste into ChatGPT.
If you want, next edition I will publish 10 tested meta-prompts for content, outreach, ads, analytics, and code tasks.
See you in the next issue.
If you enjoyed this one, hit reply with “Awesome” and tell me What would you like me to cover in upcoming Newsletters?
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