Nate B Jones presents a 10-level AI fluency framework to help users assess and improve their LLM skills
A solo presentation by Nate B Jones outlining a model-agnostic framework for assessing AI fluency across ten levels.
Summary
Nate B Jones addresses one of the most common questions he receives — how to level up in AI and measure progress — by presenting a 10-level LLM fluency framework that is agnostic of specific tools or models. Rather than listing discrete skill sets tied to particular platforms or job roles, the framework focuses on underlying principles, mental models, and systems thinking. Jones argues that most people currently sit below level five, and that AI fluency is a generalist skill set that is being widely undertaught. He closes by framing the framework as a living tool, not a static artifact, urging listeners to map emerging AI developments — such as new agent frameworks — onto their own position on the scale.
Key Takeaways
FULL TRANSCRIPT
Introduction: The Problem with How AI Fluency Is Taught
Nate B Jones: One of the questions I get the most is, "How do I level up in AI? And how do I know that I'm doing well, or at least improving?" There hasn't really been a comprehensive approach that is agnostic of models — that doesn't care if you're a ChatGPT user or a Copilot user or a Claude user. It just focuses on the principles and your level of understanding, and helps you to scale it up. That's what I'm doing here.
In this video, I'm going to walk you through how to tell roughly where you are on the overall LLM fluency scale. Spoiler alert: most people end up below five. This is a tough scale — don't be afraid. Then I'm going to walk you through what it looks like to actually improve.
There's a lot more to dig into in the post. I have a comprehensive assessment prompt, and I also have a prompt for building yourself a 90-day custom development plan for wherever you're at. So there's lots to dig into. But first, let's understand the levels and what they mean.
I'm not going to cover all ten, because people just don't stick around for that. I'm going to give you blocks — a one to three, a three to five, that kind of thing — and we're going to go pretty quickly.
Levels One to Three: Basic Beginners
So, level one: basic beginner level. Most people are here. That's your default. If you are a ChatGPT user, a Copilot user — if you're the kind of person who uses these AI tools to rewrite emails or adjust a document — you're probably in this one-to-three area.
I want to emphasize again: this is not a bad or good thing. This is just helping you understand where you are so that you can figure out where you want to go and what your goals are. Not everyone has to be a ten. That's not the point. The point is to understand your level and your goals, and make sure you are equipped to get there. That's what I'm all about.
Levels Three to Five: Building Mental Models
Let's jump ahead to levels three to five. What does that look like?
At three to five, you are starting to build a mental model for AI. And this is why this scale is so important — no one really talks like this. People tend to give you specific skill sets, and I can do that too. I'm going to talk about some of the specific skill sets you demonstrate. But you need an overarching perspective on the fluency and competency assessment you're looking for at this level.
Three to five is all about building mental models. You are starting to understand how LLMs actually work — what they do when they reason, what they do when they don't reason. You're starting to understand that LLMs don't truly "know" things, that they're not programmed in the traditional sense. You're understanding what next token prediction looks like. You have the beginnings of a mental model of what AI can do.
One of the things that is more important these days than it used to be is understanding context retrieval. It used to be that those "understand how LLMs work" lessons were enough. But now, as AI has gotten more powerful, you actually do need to understand the ability to retrieve a larger piece of context and work with it. These AIs can take book-sized prompts now — book-sized context windows. So you need to understand how that works and have a mental model for it too.
I should add: none of this means you can build an AI system. None of this means you can build a context window like a RAG system or a memory system. If all of that is above your head, you're still firmly at three to five if you have the mental model down.
The last piece I want to call out from a mental model perspective is that this conceptual understanding of AI is going to naturally lead you to thinking backwards from outcomes. You're going to stop asking, "What should I tell the AI?" and at this stage you're going to start asking, "What is the output that I need?" — because the mental models are going to inform your understanding of how it creates outputs, and you're naturally going to start to say, "I get a sense of how the sausage is made, so this is the output I want, and I can work back in my head."
This is how you start to get to what I would call intuitive prompt engineering. You're not reading from a book. You're not necessarily copying a prompt — maybe you do sometimes, maybe you don't. And even if you do, you know how to massage and tailor it. Or you can write it yourself. Either way, you know how to get to the outcome you want.
So many people are here. What I just described — the level one and two basic users of Copilot or ChatGPT, plus this level of understanding LLMs through mental models — that's almost everybody. If you want to talk about 80/20, 80% of people are right there.
Levels Five to Seven: Systems Thinking
Now, what goes on above those levels? I'm going to make this as accessible as possible, and I'm going to give you a sense of whether you need to go farther based on your goals.
From five to seven, you are probably going to be working with AI on a professional basis very seriously. If you get above five — above this mental model stage — there are some patterns that start to come through that you just don't see at a lower fluency level.
The overall approach is systematization. You are using systems thinking moving from five to seven. A person between five and seven is going to be thinking in auditable patterns with AI. They're going to move from "I usually do this" to "this is the sequence I follow — I get a predictable result, I know how to get that predictable result, and I can start to systematize it in a way that others can do it too." It's not just intuition at that point. It's an actual understanding of how the system works so that you can predict and move with it.
Another example of systematic thinking is building for prompt yield. Prompt yield is essentially: what is your quality output per unit of prompting? If you're prompting inefficiently, you might take ten iterations to get one usable output. But if you're prompting efficiently, you might do one or two prompts and get 98% of the way there and move on.
This is part of why I emphasize the kinds of prompts I emphasize in my posts. It's really important to value the tokens and the time we're spending with AI so that we can go on to other things. It is much more efficient to just do the prompt correctly and get the right answer. Someone who is building and thinking in systems is able to understand that, and is able to move from a casual, intuitive "I think this is the right prompt" to a systematic "this is the yield I get on this prompt, I think it can be modified in these three ways, I'm going to get a much more efficient output" — and then they make the change, measure it, and see it.
These kinds of people think in feedback loops. Your systems are working to make you more effective at AI. It doesn't necessarily mean you have to have tons of tools, but in my experience, most people at this stage will have a prompt library. They will have five to seven tools they're working with regularly in the AI space. They will have preferences for specific work tasks associated with those tools. And they will be seen by their teammates as a peer collaborator and peer leader who can help the team put in place systems that matter.
You'll notice these are not job-specific. I am not giving you the fluency levels for engineers and then separately for PMs. And the reason is that I have a strong conviction that AI is a generalist skill set, and we are probably teaching it wrong if we dive too deep into verticals without that generalist conceptual foundation. We really haven't had that foundation, and that's what I'm setting out to build here.
It's great if you understand how to build with LangSmith as a developer, but I don't think that's the only kind of AI learning and grounding you need. We're missing this piece — this general approach to skill sets and fluency. Having a common understanding here will be helpful.
Levels Seven to Nine: Teaching and Innovation
Let's jump to seven to nine. At this point, you've mastered systems thinking. You understand how LLMs work. You are a teacher and a trailblazer. You need to start thinking about who you can teach with your skill set, and how your teaching drives your own learning.
For me, teaching has been super helpful in driving clarity and revealing gaps in my own understanding that I have to relentlessly close. Most teachers will tell you that's true regardless of subject.
Try to be a documentarian at this level. The more you document what you're learning, what you're thinking, and how you're growing, the more you're able to scale your influence and teach others. It's not about growing influence for its own sake — it's about being able to communicate clearly things that are net new in the space, and then understanding how to teach those things to others in a way that is accessible for their level.
That might look like setting up the AI training curriculum at work. It might look like leading a group of developers through their first AI build. It might look like what I do here — on YouTube or Substack, talking through what it means to grow and learn AI. There are lots of ways to do this.
The systematic thinking doesn't go away. You're not just thinking in team systems or personal systems — you are often doing something public that many others can use. You might be building Claude projects that others can use. You might be building a little vibe-prompted tool that others can use to understand their own level of fluency — similar to what I've done with the prompts in this piece.
Your goal is to pull impossible problems into the realm of the possible. That's what the innovation piece looks like. Someone who is teaching, learning, and growing should be helping to pull forward things that were previously deemed very difficult to do with AI, because they are helping to discover AI capabilities.
Understanding that AI capabilities are not all documented — that you can discover them and put them to new uses — is a great example of what the relationship between systems thinking and deep understanding of LLMs is all about. People who understand LLMs deeply know that LLMs are not all discovered. We do not ship an AI and have OpenAI know everything about it. We ship an AI and we all collectively discover the capabilities it has, because it's more accurate to say these systems are grown than to say they are programmed. We're all discovering together what grew.
That is part of the job at level seven to nine: to start to innovate, understand where to push farther on LLM capability and why it matters, and then be able to turn around and teach that back and really grow the practice.
On Level Ten — and the Shifting Baseline
I don't give tens. There's not going to be a ten here.
One of the things I want to call out is that wherever you are, your competitive reality is shifting. We are in October of 2025, not too far away from the end of the year and heading into 2026. You need to think of your baseline as shifting into the new year — the whole population is going to grow into the one-to-three range in the next year, and there will be a much larger part of the population growing into that three-to-five area, with many more people pushing themselves up the skill ladder from there.
What I'm saying isn't meant to panic you. Your goal may not be to be a teacher or an instructor. Maybe your goal is to be a systems thinker. Maybe you're perfectly happy just understanding how LLMs work. But regardless, I want you to recognize that the skills required at each stage are evolving as you go.
My best advice — and I think I've said this before — is to think of it as a moving train, and it's never going to go slower than it's going right now. So hop aboard and get yourself going as quickly as you can, in a way that feels comfortable and aligned with your goals.
Mapping the Framework to Future AI Developments
Just as I said that three-to-five people developing mental models of AI are starting to understand the technology, develop a mental model of your own career path. Have a sense, within your job family, of what level of fluency would be useful. And then here's the extra credit — come back to this in 2026, because you're going to want it.
Think about the corresponding skill sets that will emerge and map them onto this fluency chart. That can feel abstract, so let me give you an example. Think about AI agents. On October 6th we had the launch of a new kind of agent framework from OpenAI. Think about how the fluency types map onto that.
Systems thinkers are going to think about how you build not just one agent but multiple agents, and how you sustain them within an organization. Innovators are going to think about new things you can do with agents. People just understanding LLMs are going to think about what is an intuitive way to get a task done that helps them express their understanding of LLMs and get real work accomplished. And people who are just starting out are going to scratch their heads and say, "This agent thing looks really hard."
But you can map that entire technical launch onto this capability assessment — and you can do that with other launches that are coming forward too. This is not meant to be an October 2025 artifact that we're done with. It is actually meant to be a living, breathing framework that helps you make sense of your own skill level relative to where we are with AI — something you can come back to again and again.
So there you go. That is my evergreen AI fluency assessment. As far as I know, we haven't really talked about things like this before, or certainly not in this way.
I hope it's helpful. I'd love to hear where you're at, and most of all, where you want to get to. That's one of the things I was really excited about for this piece — putting together a sense of the ladder, for lack of a better term. It's really not a ladder, but a sense of the jungle gym of AI, and a sense of where people can go. Drop a note and let me know.