|
Almost Timely News: 🗞️ What’s Changed in Basic AI Prompting (2026-09-20) :: View in Browser
 |
The Big Plug
✍️ Enroll in my new course, AI for Writers and learn how to make AI write better.
📺 Watch my 25-minute Intro to GEO talk at Spotlight Summit!
Content Authenticity Statement
100% of this week’s newsletter was made by me, the human. Learn why this kind of disclosure is a good idea and might be required for anyone doing business in any capacity with the EU in the near future.
Watch This Newsletter On YouTube 📺
Click here for the video 📺 version of this newsletter on YouTube »
Click here for an MP3 audio 🎧 only version »
What’s On My Mind: What’s Changed in Basic AI Prompting
This week marked the return of conference season and me being on the road several days a week for the next couple of months. I’m grateful always for the chance to get out and talk to actual humans instead of just machines, especially when those humans are friends.
As I talked to many, many folks about how they’re using AI these days, one thing became abundantly clear - changes in how models work, especially this year, have not filtered down to how users are using AI. Too many folks are using knowledge from the early days of AI that isn’t useful any more.
So this week, let’s dig into what some of those changes are.
The Big Shift
The big change over the last calendar year - summer 2025 to summer 2026 - is in models becoming more agentic. Now that’s a word that gets thrown around a lot, agentic this and agentic that. What does it mean, and what does that tell us about how to use AI?
Agentic models mean that AI models have been tuned and optimized to do stuff by themselves, as autonomously as possible. When you hear fancy phrases like “long horizon model”, all that means is that AI models have been trained to do as much as they can on their own.
From 2021 through 2025, AI model makers focused on the models knowing more, learning more, training them on ever more data to make them smarter. The idea of the model was almost like a library, and everyone was racing to put more on the shelves than the next nearest competitor.
That broke down, though. The more knowledge you put, the bigger the library has to be, the more time it takes to walk up stairs and through the stacks to find the knowledge you’re after. Making models ever bigger slowed them down and makes them cost more in terms of power and resources.
As AI became more agentic, more autonomous, model makers started to pivot how they’re trained. Instead of trying to make them smarter and smarter, they changed what AI models should do, making them more like engines. AI models focus less on knowing and focus more on doing.
This is visible and prominent in models like Google Gemini 3.8 Flash, the model that powers Google Search and many, many other Google services. Gemini 3.8’s knowledge cutoff is January 2025, a full 20 months ago. Why? Because Google’s focused on making Gemini more skillful and injecting knowledge upstream from it in all its tools.
Likewise, folks in the local AI subreddits noted that the latest version of Qwen, Qwen3.8, was significantly less knowledgeable about granular, specific details (such as geography) than previous models. It turns out that Alibaba, the makers of Qwen, reduced or removed book knowledge in favor of enhancing tool handling.
That’s the big shift our brains have to make - less about knowing, more about doing.
How does that translate into the ways we should be using AI today?
Principle 1: Knowing is Separate from Doing
When we use modern AI models, if they’re less about knowing and more about doing, then the responsibility of providing knowledge has to be either upstream or downstream from the model.
If we use one of my favorite analogies, kitchens, AI models are very much like expensive high-end appliances. You don’t store food in your oven or microwave, except maybe for warming and staging while you’re cooking. Your ingredients are upstream of the appliance - pots, pans, pasta, water, etc. - and then how you use the outputs from the appliance - a pasta dinner - are downstream from the appliance. Stoves don’t normally just have lots of pasta waiting around to be cooked, sitting on a pot on the burner for days on end.
That’s how we have to change our thinking about AI. If we pretend the model knows nothing but is capable of doing all kinds of things, we have to provide the knowledge either before the model, or take the model’s output and do something else with it afterwards (and usually both).
What does that mean for you? It means if you’re not providing AI with knowledge, you’re going to get subpar results. You need to be providing the knowledge along with your prompts, be it documents, databases, multimedia - anything that gives AI clear specifics.
Thus our first key modern AI prompting principle is that we bring the knowledge, AI brings the action, not the other way around.
One side consequence of agentic model tuning that’s worth pointing out, especially for marketers and creative folks, is that as models are focused more on doing than knowing, they’re inherently less creative. More than a few folks have pointed out that the current generation of models feels like they’re less creative writers and out of the box, they are. How to fix that? You have to provide clear guidance and knowledge, which I’ll cover in principles 3 and 4.
Principle 2: Doing Involves Tools
Our second major principle is that almost all modern models are tuned for what’s called tool handling. That means they’re aware of tools and will use them rather than trying to do tasks themselves that they’re poorly suited to perform.
There are two tools that almost every AI model reaches for immediately - Python code and web search. Any time an AI model is asked to do something like math these days, they’re smart enough now to know they can’t do math reliably. They almost all invoke what’s called a code interpreter, aka a coding environment inside where the AI is hosted, to read and write code, and the most common language they all speak is Python.
To be clear, you the user don’t have to know a stitch of Python. But if you ask AI to do some math, there’s a very good chance it’s now just doing some coding behind the scenes to get you the answer.
In the more advanced harnesses - the applications that we use models in, like ChatGPT Work, Claude Cowork, Microsoft Copilot Cowork, etc. - AI models have access to a buffet of tools like local virtual machines, entire computers that they can use on our behalf. Google’s Gemini Spark, for example, can spin up a virtual machine just for itself to browse the web and install its own software.
The second tool that AI reaches for frequently is web search. When we ask questions where the model isn’t confident in its knowledge, or when we explicitly ask it to, AI models will conduct web searches. This is especially true for chats and prompts where fresh knowledge is necessary, but the user doesn’t provide it.
As an aside, a lot of people are losing their minds over GEO/AEO/AI Visibility, and a lot of vendors are producing a ton of snake oil about the topic. The reality is this: in an agentic AI world, AI knows to do web searches and knows how to do web searches. That in turn means tha