Mind Readings: Specificity is Scarcity

Mind Readings: Specificity is Scarcity

In today’s episode, you’ll explore the principle of “specificity means scarcity” in the context of generative AI. You’ll learn how the level of specificity in your requests influences the amount of data required for AI models to generate accurate and relevant outputs. Additionally, you’ll gain insights on understanding model sizes and their impact on knowledge depth, and how to provide sufficient data for specialized tasks.

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Christopher Penn: In today’s episode, a maxim that I want you to remember: Specificity means scarcity.

When it comes to generative AI, specificity means scarcity.

When you’re working with generative AI, the more specific you are, the more scarce the model’s knowledge is, which means the more knowledge, the more data you will have to supply.
For example, you go up to a tool like ChatGPT, say, “Hey, write a blog post about B2B marketing strategies”—it’s going to have no problem writing something.

It may not be great, it may be very generic and bland and boring and not at all unique, but it’s going to have no trouble doing that because there’s a lot of general knowledge at that level.
If you said, “Write a blog post about how Trust Insights’ particular implementation of WordPress feeds its B2B marketing strategy”—it’s gonna have no idea.

It’s like, “I don’t know, how do you use WordPress?” You will need to provide most of the data to answer that question because no model on the planet knows what we have installed in our WordPress instance.

Some days, I don’t know what we have installed in our WordPress instance.

But the point being, it’s a very specific question.
What makes good content? Specificity.

If you have an ideal customer profile of maybe just six or seven people—or maybe six or seven people—you’re gonna have to provide it.

You’re gonna have to provide it to those who are left-handed, Black, lesbian women, you’ve got a super specific, super specific—there may not be enough data on the internet to even build an ideal customer profile from that.

So you’re gonna have to provide it.
This is true broadly, but it gets even more true the smaller a model is.

You’re going to be—you’re going to hear a lot about models of different sizes over time.

Llama comes in eight billion parameter and 70 billion parameter versions; there’s a 400 billion parameter version coming.

When you hear “parameters” in relation to models, it basically means how much statistical knowledge it has.

The more it has, the more capable it is.
A tool like Google’s Gemini or Anthropic Claude or ChatGPT—these models have in the many billions to trillions of parameters.

And so they’re—they have a—they’ve seen a lot.

They’ve seen a lot.

Llama, the eight-billion-parameter model, knows a lot about general stuff and knows nothing about specifics.

I can ask Gemini, “What do you know about the consulting firm Trust Insights?”, and it’ll come up with a factually correct answer.

I ask the same question of Llama, it has no clue whatsoever.

And so you need to know what’s inside of a model and then be willing to provide the data it does not have.
If you look at the Trust Insights PAIR framework, if you go to TrustInsights.ai/pair, you can download the PDF for free—no forms to fill out.

The first part of that framework is Prime, which is when you ask the model, “What do you know about X?”, right? If I said to the model, “What do you know about the consulting firm Trust Insights?”, and it clearly spits out wrong answers, I know that I need to provide a lot of information, I need to provide a lot of stuff because it just does not know.
So, specificity means scarcity.

If you—the more specific the task you’re trying to perform with AI, the more data you’re going to need to provide if you want that task to go well.

That’s gonna do it for today’s episode.

Thanks for tuning in.

We’ll talk to you soon.

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