This Meta-Prompting Hack is INSANE! 🤯

This Meta-Prompting Hack is INSANE! 🤯

Ahoy, prompt adventurers! 👋 Are you ever stuck in that loop of endlessly tweaking prompts, trying to hit gold? I’ve been there, pulling my hair out! But hold onto your hats, because I’ve just stumbled upon a meta-prompting workflow that feels like an absolute cheat code, and it’s too awesome not to share!

Instead of us doing all the hard work, we get the LLM to generate AND evaluate prompts. It’s like having the LLM optimize itself! Seriously, the results are surprisingly effective.

⚙️ Here’s the Game Plan:

I was blown away by how simple yet powerful this is. Here’s the step-by-step:

1. 📌 Step 1: Tell your LLM something like: “Whip up a detailed prompt engineering guide for [your target audience, e.g., awesome book authors, super-smart software devs, or top-tier customer support folks]!”
2. ✅ Step 2: Feed it 5 solid input-output examples. Show it exactly what you want your final prompt to do, the kind of magic you’re aiming for.
3. 💡 Step 3: Now for the clever bit! Ask it: “Based on those examples, generate a prompt that would produce these outputs, AND make my examples even better.”
4. 🔄 Step 4: Open a brand-new chat with your LLM. Ask it: “Alright, genius, now generate a detailed prompt evaluation guide for that same [target audience] we talked about earlier.”
5. 🎯 Step 5: Take the prompt the LLM generated back in Step 3, paste it into this new chat, and ask the LLM to evaluate its own creation using the guide it just made in Step 4. See? It’s evaluating itself!
6. 🚀 Step 6: You’re nearly there! Now, command it: “Based on your awesome evaluation, generate 3 improved versions of this prompt.”
7. ✨ Step 7: Pick the shiniest, best prompt from those three options. Give it a final polish if you need to, and boom – you’ve got a highly optimized prompt!

🤯 Why This is So Darn Effective?

 

 

It’s pretty genius, actually! You’re basically using the LLM’s own internal wiring and smarts to build prompts that are perfectly tuned for how it “thinks.” It’s like having an insider create the instructions! You’re setting up this awesome feedback loop where the LLM generates, then judges its own work, all within the same system. Supercharged stuff!

This method has seriously upped my prompting game, and I bet it’ll supercharge yours too.

For all the juicy details and the original insights from matan12b, you’ll definitely want to check out the full Reddit post!

A meta-prompting workflow that drastically improves any prompt (using the LLM to optimize itself)
byu/matan12b in