The Concept: Let the AI Talk to Itself
A few years ago, I bought a house with around twelve acres. During COVID, I got busy planting 100 trees to start my master plan of a backyard arboretum.
The real challenge has been keeping it mowed. Initially, mowing took about six hours, but I’ve cut that down to three and a half with my new John Deere.
Now, you might think that three and a half hours of mowing a week is a lot (and it is), but I wouldn’t trade it for the world.
Why?
Because for me, it’s time to decompress and think.
I wear my Bluetooth headphones to drown out the noise of the mower.
Sometimes I’ll listen to music or maybe an audiobook. But often, I’ll just mow in silence, thinking. I’ll loop over different business projects I’m working on and figure out my next steps.
By the time I’m done mowing, my thoughts are in a much better place than when I started. And I wouldn’t have reached some of my decisions if I’d only spent 15 minutes in contemplation.
So… how does this relate to AI?
Well, a common prompting technique is back-and-forth iteration. It’s when you critique the AI’s output and have the AI improve on it. Usually, this involves a long, drawn-out back-and-forth conversation.
“Make it more engaging”
“Add some personality”
“No, that’s too casual”
“Include more data”
“Now make the CTA stronger”
“Start over, but keep the data part”
Sound familiar?
Using this approach can lead to a high-quality end result, but it takes a long time to get there. Plus, you don’t have an easy way to replicate the result because there wasn’t a “single” prompt that created the output. Instead, it was formed from your entire chat history.
So while there’s definitely a time and place for manual back-and-forth refinement, there’s also a way to skip it and still achieve a high-quality output with a single prompt.
I call it the self-imposed prompt loop.
No more back and forth.
No more not knowing what prompt created the output.
Just a great result from a single prompt.
It’s kind of like giving the AI some headphones to drown out the noise and getting it to critique itself, all with a single prompt.
Let me show you how it works.
How it Works: A Self-Imposed Prompt Loop
The self-imposed prompt loop is a hack that takes advantage of how Large Language Models (LLMs) generate text. They don’t generate text all at once. Instead, they create it one token at a time (for sake of explanation just think of a token as a word), building on what they just wrote to figure out what comes next. This is called autoregressive generation.
(If your brain’s starting to hurt, don’t worry; the pain’s almost over.)
Since autoregressive generation predicts what comes next based on the sequence of tokens that came before it, we can hack the AI to critique its own work as it goes along, all within the same response!
Let’s start with a prompt that doesn’t use this self-imposed prompt loop technique:
AI Prompt
Updates:
– New AI task prioritization feature that analyzes team workload
– Integration with Slack and Microsoft Teams (launched last week)
– 30% faster load times after our infrastructure upgrade
– Customer case study: How Nike reduced project delays by 40%
Draft our monthly newsletter email covering these updates.
AI Response
Subject: Big news this month! 🚀
Hey there!
We’ve been busy making some awesome improvements, and I can’t wait to share what’s new.
Smart task planning that actually works
Our new AI feature looks at your team’s workload and figures out what should come first. No more guessing or juggling priorities, it just handles it for you.
Now you can connect with Slack and Teams
We launched this last week and people are loving it! You can now sync everything with the apps you already use every day. Makes life so much easier.
Everything’s way faster now
Remember when things felt a bit slow? Well, we fixed that. After upgrading our whole system, pages load 30% faster. You’ll definitely notice the difference.
Nike cut their delays by 40%
Speaking of wins, Nike shared how they’re using our platform to keep projects on track. They went from constant delays to finishing 40% more projects on time. Pretty cool, right?
That’s what we’ve been up to! Let me know what you think about these updates.
Talk soon,
The Team
So that’s what you get from just a standard prompt, without using the self-imposed prompt loop.
What do you think?
I think… it’s way too generic.
If I got this email in my inbox, I’d read the first sentence and then hit “trash.” I might even unsubscribe after getting too many of these boring emails for a few weeks.
Now, we could manually go through a back-and-forth a few time to have the AI improve it (fix the weak subject line, make the opening less generic, add a value proposition, etc). But our goal is to have the AI do all of that automatically, right in the original prompt.
So let’s see how we can accomplish this with a self-imposed prompt loop:
AI Prompt
Updates:
– New AI task prioritization feature that analyzes team workload
– Integration with Slack and Microsoft Teams (launched last week)
– 30% faster load times after our infrastructure upgrade
– Customer case study: How Nike reduced project delays by 40%
Do the following:
1. First, draft our monthly newsletter email covering these updates
2. Then, act as a content strategist and critique it.
3. Finally, rewrite the newsletter incorporating all your critiques.
Do you see what we did differently?
Inside one prompt, we ask the AI to write a draft of the newsletter. But we don’t stop there. In the same prompt, we ask it to then critique what it wrote, and then implement the changes.
This ONLY works because we’re asking the AI to actually write the draft and then critique it.
If we had instead asked the AI to “think” of what it would write, and then critique its thoughts, it wouldn’t work.
Why?
Because autoregressive generation wouldn’t get triggered! It only works based on what the AI has previously written!
Let’s check out the result:
AI Response
1. DRAFT NEWSLETTER
Subject: Big Updates This Month – New AI Features & Major Integrations
Hi [Name],
Hope you’re having a great month! We’ve got some exciting updates to share with you.
What’s New:
🤖 AI Task Prioritization – Our new AI feature analyzes your team’s workload and suggests task priorities to help you work smarter.
💬 Slack & Teams Integration – You can now connect your favorite messaging apps directly to our platform. This launched last week and the feedback has been amazing!
⚡ Faster Performance – We upgraded our infrastructure and you’ll see 30% faster load times across the platform.
📈 Customer Success Story – Check out how Nike reduced their project delays by 40% using our platform. [Read the full case study here]
That’s all for now! Let us know if you have any questions.
Best regards,
The Team
2. CONTENT STRATEGY CRITIQUE
Major Issues:
– Weak Subject Line: Too generic and doesn’t highlight the most compelling benefit
– Poor Structure: Lacks hierarchy – the Nike case study should be the hero story
– Missed Storytelling Opportunity: Each update is presented as isolated facts rather than part of a cohesive narrative
– Weak Value Proposition: Doesn’t clearly connect features to customer outcomes
– No Clear CTA: Readers don’t know what to do next
– Generic Tone: Sounds like every other product newsletter
– Poor Email Flow: Should follow problem → solution → proof → action structure
Specific Improvements Needed:
– Lead with the Nike success story as social proof
– Connect all updates to a central theme of “efficiency and collaboration”
– Add specific CTAs for each segment
– Use more compelling, benefit-focused language
– Create urgency around trying new features
3. REVISED NEWSLETTER
Subject: How Nike Cut Project Delays by 40% (+ Your New Efficiency Tools)
Hi [Name],
What if you could cut your project delays by 40%?
That’s exactly what Nike accomplished using our platform. Their secret? Better task prioritization and seamless team communication.
The best part? We just made it even easier for you to achieve similar results.
🏆 CUSTOMER SPOTLIGHT: Nike’s 40% Improvement
Nike’s project management team was struggling with competing priorities and scattered communications across tools. Sound familiar?
After implementing our platform, they:
✅ Reduced project delays by 40%
✅ Improved cross-team collaboration
✅ Gained clear visibility into workload distribution
[Read Nike’s full transformation story →]
🚀 NEW: Your Personal AI Task Strategist
Ever wish you had a project manager analyzing your team’s workload 24/7? Now you do.
Our new AI prioritization feature:
– Analyzes your entire team’s capacity in real-time
– Suggests optimal task sequencing
– Identifies bottlenecks before they happen
Result: Spend less time in status meetings, more time delivering.
[Try AI prioritization now →]
💬 JUST LAUNCHED: Native Slack & Teams Integration
Stop switching between apps. Your project updates now flow directly into the conversations that matter.
– Instant notifications in your preferred messaging app
– Comment and update tasks without leaving Slack/Teams
– Full context sharing with external stakeholders
Early users report saving 2+ hours per week on status updates.
[Connect your workspace →]
⚡ Behind the Scenes: 30% Speed Boost
While you weren’t looking, we rebuilt our entire infrastructure.
The result? Everything loads 30% faster. Your dashboards, reports, file uploads – all of it.
Because waiting for software is time you can’t get back.
Ready to achieve your own 40% improvement?
These updates work best together. Here’s how to get started:
– [Set up AI prioritization →] (2 minutes)
– [Connect Slack or Teams →] (1 minute)
– [Book a strategy call →] to discuss your specific goals
Questions? Just reply to this email – we read every response.
To your success,
[Name]
The [Company] Team
P.S. Want to be featured in our next customer spotlight? [Tell us your success story here →]
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Now that’s a BIG difference from the first prompt result!
The best part?
The AI created it with just one prompt. So we saved tons of time without all that needless back-and-forth. We also have our single prompt that wrote the output so we can use it again later.
I want to mention something though. The input prompt above is a very bare-bones version of the self-imposed prompt loop because my goal was to show you how it works.
When you actually use it in your workflow, you’ll probably want to give the critique some guidance based on what your company or client wants. But you should be able to make it generic enough that you can save your prompt as a blueprint and then pull it out each time you need to use it again with different data.
Pro Tips
Be Specific About The Critique: Don’t just say “critique this.” Give it some guidance on what you’re looking for based on your goals.
The Sweet Spot is 1-2 Loops: One revision cycle usually gets you 80% there. Two gets you to 95%. Any more than that and the output quality can degrade.
Try Different Critique Perspectives: You don’t just have to tell the AI to critique the output based on some criteria. You could also have the AI critique the output based on a particular perspective, like as a CEO of the company, as a marketing agency, or as an experienced copywriter.
Save Your Best Loops: When you find a role combination that works perfectly, save it as a template. Your future self will thank you.