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The AI Daily Brief: Artificial Intelligence News and Analysis

5 Prompting Tricks to Make Your AI Less Average

The AI Daily Brief: Artificial Intelligence News and Analysis

Nathaniel Whittemore

Technology

4.7763 Ratings

🗓️ 19 October 2025

⏱️ 21 minutes

🧾️ Download transcript

Summary

AI is trained on the sum total of human output—which means it often produces the average of averages. That’s fine for passable results, but not for unique, high-quality work. In this weekend big think episode, NLW explores what he calls AI’s tyranny of the average and shares five techniques to break through it: using negative style guides, forcing divergence and choice, burning down clichés, prompting self-critique, and leveraging examples that defy consensus. Based on an essay by Alex Kantrowitz, this is a practical guide for anyone who wants their AI outputs to stand out rather than blend in.

AI Sameness Essay: https://www.bigtechnology.com/p/ais-sameness-problem

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Transcript

Click on a timestamp to play from that location

0:00.0

Today on the AI Daily Brief, how to make your LLM not average.

0:05.7

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

0:17.8

All right, friends, quick announcements before we dive in.

0:20.0

First of all, thank you to today's sponsors, Super Intelligent robots and pencils, Notion and Blitzy. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief, or you can subscribe on Apple Podcasts. And to learn about sponsorship opportunities, shoot us a note at sponsors at AIdailybrief.aI. All right. So today's episode is something I've been thinking about for a while.

0:40.0

In my head, I've always called it AI's tyranny of the average.

0:44.1

And the simple notion here is that because AI has been trained across the entire corpus of everything that humans have output, almost by definition, it is optimized around average conventional

0:56.1

wisdom. Sometimes that's fine. What that does is that it ensures that the output of an LLM

1:02.5

has a fairly high floor. If it produces passable content, passable writing, passable imagery,

1:09.1

based on how you prompted it, that's good, right? At least it gets you in the zone. The problem is that increasingly, when it comes to production use cases and using AI for things that really matters, average isn't good enough. We want more than average. We want unique. We want distinct. We want really high quality. And for this, we have to turn to some prompting strategies, five of which I'm

1:28.4

going to share today, that I have found help me in the ways that I use LLMs to make them

1:33.7

excel ahead of that average output. Now, one of the reasons that I thought this would be a good

1:38.3

fit for the weekend big think slash long reads episode is that technology writer Alex

1:42.9

Cantrowitz actually dropped a little

1:44.5

quick essay on his blog, big technology.com, this week that's pretty much about this. He called it

1:50.3

AI sameness problem. And it's short, so we'll read it quickly, and this will be me reading, not

1:54.9

AI. For better or worse, you guys have sent the message clearly that as good as AI voice technology

1:59.8

is, you prefer me reading it,

2:01.3

and that's fine. But we'll read Alex's essay, and then we'll talk about these five techniques

2:05.1

that I have found to work for overcoming the problem of AI's averageness. Again, his essay is called

2:10.8

AI's sameness problem, and it reads, Open AI's video generation app SORA sits atop the App Store

2:16.7

charts, but I anticipate it'll fall off soon.

2:19.9

Creating SORA videos is a genuine but momentary thrill.

...

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