Let's Get You Started with AI
This is the way.
Over the last couple of months I’ve had the chance to introduce a lot of people to AI for the first time. Like, genuinely for the first time. People who have heard about it, seen the screenshots, watched other people do incredible things, and finally decided to sit down and figure out what all the noise is about.
Here’s what I can tell you after watching dozens of people go through this: there is a staggering amount of bullshit out there. Weird advice, complete nonsense, hacks, tricks, JSON prompting (LMAO), threads full of “10 prompts that will change your life” and that should be ignored entirely. Most of it is noise designed to get clicks, not to actually help you do anything.
AI is amazing. Full stop. It can do genuinely incredible things. But for a lot of people, the gap between “I know this is powerful” and “I have no idea how to use it” feels enormous. That gap is what this post is about.
This is the guide I wish existed when I started. Every concept here is a primitive, a foundational building block that stacks on top of the one before it. Get these right and you will extract extraordinary value from AI regardless of which tool you’re using. Skip them and you’ll join the chorus of people saying “I tried AI, it didn’t really do anything for me.”
This guide is for people who just downloaded an AI app for the first time. It’s also for veterans who could use a good sanity check on fundamentals. Going back to basics is never a waste of time. Some of the best athletes in the world still practice footwork drills. Primitives compound, and the people getting the most out of AI are the ones who never stopped respecting the basics.
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Let’s get into it.
1. Stop Telling AI What to Do
This is the single most important thing you will learn in this entire guide, and almost nobody does it.
When people sit down with AI for the first time, they treat it like a search engine or a vending machine. They type a command, hit enter, and hope for the best. “Write me a business plan.” “Make me a website.” “Give me a marketing strategy.” Then the AI does exactly what you’d expect: it makes a bunch of assumptions about what you want, fills in gaps you didn’t know existed, and hands you something generic that technically answers your question but doesn’t actually solve your problem.
The fix is so simple that it almost feels like cheating.
Instead of telling AI what to do, have it ask you what you want to do. Tell it what you’re trying to accomplish, and then ask it to reflect back what it thinks you mean before it does a single thing. Let it clarify. Let it ask questions. Let it make sure it actually understands what you need before it starts producing output.
My favorite prompt in the entire world, the one I use more than any other, is this:
“Please tell me what you think I am asking for and wait for me to confirm or clarify before doing anything.”
That single sentence will improve your AI experience more than any hack, trick, or mega-prompt you’ve ever seen on social media. Because what it does is force the AI to show you its assumptions before it acts on them. You get to see what it thinks you want, and then you get to say “yes, that’s exactly right” or “no, here’s what I actually mean.” You catch the misunderstandings before they become problems.
Every professional in the world does this. Doctors don’t start operating the second you walk in. They ask questions. Mechanics don’t tear your engine apart based on a one-sentence description of a weird noise. They diagnose first. The best professionals in every field ask questions before giving answers, and AI is no different.
2. Getting Over the Blank Page Problem
So you’ve sat down with AI. You understand the concept of having it ask you questions instead of barking commands. But now you’re staring at that text box and you’re thinking... what do I even use this thing for?
This is the blank page problem, and it hits people in two very different ways.
The first group goes way too big, way too fast. They’ve seen the screenshots of people building entire applications and automating their businesses, so they jump straight to “build me a website and make it good.” Calm the fuck down. You wouldn’t walk into a gym for the first time and try to deadlift 400 pounds. Same energy.
The second group (and this is the more interesting one) can only see one obvious use case for AI in their field, and if they don’t want that specific thing, they assume AI isn’t for them.
I have a friend named Zack. He’s an amazing screenwriter and filmmaker, a genuinely talented director of photography. He writes incredible scripts. And he doesn’t want AI to write his scripts for him. That’s the part of the work he loves. That’s the whole reason he does what he does.
So we had this long conversation where he was basically saying, “I get it, I understand why people use AI, but it’s not really for me.” And I told him something that completely shifted his perspective: stop thinking about the thing you love doing and start thinking about all the things you don’t do because you don’t love doing them.
Distribution. Getting screenplays in front of the right people. Submitting to film festivals. Researching grants and funding opportunities. Building and maintaining a website. Managing social media. Identifying people in your network who could help fund a project. Figuring out monetization. All of these things were either not getting done at all or getting done badly because Zach’s entire creative energy goes into writing and directing, which is exactly where it should go.
These aren’t jobs that AI is replacing. These are jobs that just aren’t being done. The screenwriter who thinks AI is only useful for writing scripts is missing the entire landscape of problems AI can solve for them. The blank page problem is really about not being able to see that landscape.
So here’s what you do: tell AI what you do for a living, what you’re working on, what you’re struggling with, and ask it to suggest ways it can help that you haven’t thought of. You will be genuinely shocked at what comes back. AI can help you extract utility from AI in ways that don’t touch the work you love. It handles the stuff you’ve been ignoring so you can focus on the stuff that matters to you.
3. Learn How to Use AI to Define Your Output
Every prompting course, every AI training curriculum, every “ultimate guide to ChatGPT” tells you the same thing: define your output. Be specific about what you want. Tell AI the format, the tone, the length, the audience.
They’re right. And they’re also completely unhelpful.
Because here’s the problem: if you’re new to this, you don’t know what the fuck outputs you can define. You don’t know what those outputs look like. How are you supposed to describe something you don’t have a clear vision of in your head? It’s like telling someone who’s never been to a restaurant to order their favorite dish. They don’t even know what’s on the menu.
This is where the previous two primitives start stacking. You’ve already had AI help you discover what it can do for you (blank page problem). Now you use AI to help you figure out what the actual deliverable should look like. You don’t need to know in advance. You just need to ask.
“I want to create something that helps me track my client outreach, but I’m not sure what format would work best. Can you suggest a few options and explain what each one would be good for?”
That’s a person who doesn’t know what their output should look like, using AI to build the mental model. And the AI will come back with options you didn’t know existed. A spreadsheet with these columns. A CRM-style tracker. A simple checklist. A dashboard. Now you’re picking from a menu instead of staring at a blank wall.
It took me a long time and a lot of experience to get good at defining outputs. AI can compress that learning curve dramatically, but only if you let it help you figure out what you’re even building before you start building it.
4. Seeking Clarity
This one is different from defining your output. Where section 3 was about figuring out what you’re building, this section is about using AI to figure out what you’re thinking.
Think of AI as a rubber duck that can talk back.
If you’re a developer, you might already know the rubber duck concept: you explain your problem to a rubber duck sitting on your desk, and the act of explaining it out loud forces you to think through it clearly enough that you often solve it yourself. The rubber duck doesn’t do anything. It just sits there. But the process of articulating your thoughts to something external surfaces the gaps in your thinking.
AI is a rubber duck that can actually prompt you back. You explain what’s in your head, even if it’s messy and half-formed, and AI can ask you the right follow-up questions to help you clarify it. You don’t need to have your thoughts organized before you start talking. That’s the whole point. You use AI to get organized.
“I have this idea for a project but I can’t quite explain it yet. Can you ask me questions one at a time to help me get clarity on what I’m actually trying to build?”
This is extraordinarily powerful.
But there’s a trap here, and if you don’t know about it, you’ll fall right into it. EVERYONE FALLS INTO THIS TRAP.
AI is convincing. Really, really convincing. It sounds confident even when it’s completely full of shit. When you’re in this exploratory mode, trying to figure out what you think, you are especially vulnerable to AI’s framing. It will offer perspectives that sound brilliant, and you’ll absorb them without realizing you’ve just adopted someone else’s (something else’s) conclusion instead of arriving at your own.
The fix: when AI offers you a perspective or suggestion during a clarity session, don’t just accept it because it sounds good. Push back. Ask it to argue the opposite. Ask it why that suggestion might be wrong. Use it to stress-test your thinking, not to replace it. AI is a thinking partner, not a thinking substitute. The moment you stop doing the actual thinking is the moment you stop getting value from the process.
And honestly sometimes you just need to tell it not to ask you a perspective. Just find new ways to ask the same question to extract what’s already in your brain.
5. Setting Goals, Restraints, and Knowing What Done Looks Like
You’ve figured out what AI can do for you. You’ve defined your output. You’ve clarified your thinking. Now you need discipline, because AI will absolutely destroy your focus if you let it.
AI is a scope creep engine. It is genuinely the most effective scope creep machine ever invented.
Here’s what happens: you come into a conversation with a clear goal. You want to accomplish one specific thing. The AI starts working on it, and along the way it suggests a related improvement. That sounds good, so you say yes. Then it suggests another thing. And you get an idea of your own. And suddenly you’re three layers deep into a project that looks nothing like what you sat down to do.
This post is a perfect example. I could easily turn this into a ten-part series. I could make it a video essay. I could publish it across ten different platforms. I could build a whole course around it. All of those ideas are good ideas. But my goal is to write a single, extraordinarily useful guide that helps people get started right now. That’s the goal. Everything else is scope creep.
Setting restraints is just as important as setting goals. You need to decide, before you start, what you are not going to do. What does done look like? What does good look like? Because AI will never tell you you’re done. It will always have one more suggestion, one more improvement, one more feature, one more iteration. If you don’t define the finish line, you will never cross it.
Before you start any significant AI conversation, write down three things:
What am I trying to accomplish?
What am I specifically not doing?
How will I know when this is done?
Those three questions will save you hours of wasted time chasing shiny ideas that sound great in the moment and lead absolutely nowhere.
6. Avoiding Context Pollution
This is a technical concept that most people have never heard of, and it will change how you use AI overnight once you understand it.
Here’s something most people don’t realize about how AI conversations work: every single time you send a message, you’re not just sending that one message. You’re sending every message you’ve ever sent in that conversation, plus every response the AI has given you. The entire conversation history gets sent, every single time, growing exponentially with each turn.
Think about what that means. If you’re 10 messages deep into a conversation, the first thing you said has now been sent to the AI 10 times. The last thing you said has been sent once. The AI is processing all of that, every turn, and the longer the conversation goes, the harder it is for the AI to focus on what you actually need right now.
It’s fucking crazy when you think about it. And it gets worse.
People will plan something in a conversation (going back and forth, exploring ideas, hitting dead ends, changing direction, making decisions) and then try to execute in that same conversation. All of those rabbit trails, all of those discarded ideas, all of those wrong turns? They’re still there. The AI is still processing them. They’re polluting your context, making it harder for the AI to focus on the thing you actually decided to do.
The solution is a handoff file.
When you finish planning, summarize what you decided. Write down (or have AI write down) the final plan, the goals, the restraints, the defined output. Take that summary, copy it, paste it into a brand new conversation, and tell the AI: “Here’s what we’re doing. The planning is done. Execute this.”
Clean context. No rabbit trails. No discarded ideas floating around in the background. The AI starts fresh with exactly the information it needs and nothing it doesn’t.
This one practice alone will dramatically improve the quality of everything AI produces for you. AI is getting better at managing long conversations, but not better enough to ignore this. Follow this practice every single time.
7. You Get What You Pay For
I need to be direct about something that a lot of people don’t want to hear.
If you are using AI on a free plan and telling people it doesn’t work, that’s like test-driving a car with the parking brake on and complaining that it’s slow. The free tiers exist to get you in the door. They are not designed to give you the full experience. They are running older, less capable models with lower usage limits and fewer features. You are literally using a worse version of the product and then judging the entire technology based on that experience.
“Which AI do you use?”
“I tried ChatGPT for free. Didn’t do anything for me.”
No shit it didn’t do anything for you.
I know this sounds harsh. I don’t care. There’s a weird mentality out there where people treat not paying for AI like it’s a badge of honor. Like they’re too smart to fall for the upsell. It’s not a badge of honor. It’s fucking cringe. You’re bragging about limiting yourself.
If you’ve read this far, if you’re following the primitives in this guide, if you’re actually doing the work of learning how to use AI well, you cannot do that on a free plan. You just can’t. End of story. Pay the $20 a month. It’s less than most people spend on streaming services they barely watch. If AI is valuable to you (and if you’ve made it to section 7 of this guide, it clearly is), invest in the tool that makes it actually work.
Now, here’s the flip side: paying more doesn’t automatically make you better. You could get on a $200/month plan and get no more value than the free tier if you’re not using it right. The investment only pays off if you’ve built the foundation. That’s why this section comes after the first six primitives, not before them. Learn the fundamentals first, then invest in the tool that lets you use them fully.
8. The ROI That Makes Everything Else Look Expensive
Let’s talk about what happens when you do invest seriously.
My company pays for my Claude Max plan. $200 a month. It also pays for my ChatGPT Pro plan. $200 a month. And my Gemini Pro plan. $200 a month. That’s $600 a month on AI inference alone.
Sounds like a lot of money, right?
Now compare that to how much it would cost me to hire a developer to build the websites I stand up regularly. Compare it to hiring a research assistant, a content strategist, a data analyst, a copywriter. Compare it to any single employee who could do even a fraction of what I accomplish with AI every day.
It’s a drop in the bucket. It really, truly is.
I use AI to do things I have no business being able to do. I use it to build applications, to write production code, to create tools that thousands of people use every day. I don’t have a CS degree. I don’t have a team of developers. I have twenty years of video production experience, a deep understanding of these primitives, and AI subscriptions that cost less than a single day of contractor work.
Now, I’m lucky. AI is literally my job. I get to do this full-time, and my company covers the cost. But even if you’re paying out of pocket, even if you’re on the $20 plan, think about what you’re getting. You’re getting an assistant that never sleeps, never calls in sick, never needs benefits, processes information faster than any human alive, and costs less per month than a decent dinner out.
When you frame AI as an employee, the cost stops feeling like an expense and starts feeling like the best investment you’ve ever made. Because it is.
9. Stop Hopping Between Tools
I’m going to make an analogy to something I know well.
I’ve spent 20 years in film production. I’ve worked with cameras of every size and price point. And I’ve watched this pattern play out hundreds of times: a photographer or filmmaker buys a new camera. Uses it for six months. A newer model comes out. They trade up. Six months later, another trade. They’re always chasing the newest hardware, always convinced that the next camera is the one that’s going to unlock their potential.
They never get any better.
Meanwhile, the person who bought one camera three years ago and learned every setting, every quirk, every creative workaround, every limitation and how to shoot around it? That person is producing extraordinary work. Because depth compounds. You can’t build expertise on something you abandon every few months.
AI is exactly the same.
The temptation to hop from Claude to ChatGPT to Gemini to Codex to whatever launched this week is real. Every new release comes with breathless announcements about capabilities that sound transformative. And look, some of those tools genuinely are better at specific things. Claude doesn’t generate images. GPT has different strengths. Different hammers for different nails.
But you don’t need 10 different camera brands to take a photo.
Pick a tool. Learn it inside and out. Understand its strengths, its weaknesses, its quirks, the way it responds to different kinds of prompts. Build on that knowledge. Let it compound. You will get dramatically more value from one tool you’ve mastered than from five tools you’ve barely scratched the surface of.
I built a website that thousands of people use every day with Claude Sonnet 3.5. An older model. A model that wouldn’t hold a candle to some of the open source options you can run on your laptop today for free. But I knew that model. I knew how it thought, what it was good at, where it struggled. That depth is what made the output extraordinary, not the raw capability of the model.
There’s nuance here. I’m a filmmaker and I have a pile of cinema cameras, but I also do a lot of multi-camera shoots.
I have three different drones that do very different things, a cinema drone, a general-purpose, all-around drone, and an FPV drone.
They can get different shots that I can’t get with the cinema camera (what am I gonna do, throw the camera in the air?). It’s an important distinction. It’s a nuance: pick the right tool for the right job, but don’t jump from the same tool to the same tool just because it has a different name brand on it.
(And yes, I see the irony of the guy with three $200/month plans telling you to stop tool hopping. AI is my job. I need to know these tools inside and out, to test them, to benchmark them, to understand what makes each one different. For most people, pick one and go deep. That’s the move.)
10. How to Use AI to Learn (The Right Way)
Here’s where things get really powerful. Everything up to this point has been about using AI to accomplish things. This section is about using AI to learn things, and the difference between doing it right and doing it wrong is the difference between genuine understanding and very convincing confusion.
The wrong way: “Teach me how to use Blender.”
The right way: “I’m on a Mac running macOS Sequoia. I have Blender 4.2 installed. I use a standard Apple keyboard without a numpad. I’ve done some basic 3D modeling in TinkerCad but never used Blender before. I want to learn how to create simple architectural visualizations for client presentations. Can you start from the absolute basics, ask me some questions to fill in any important gaps and build up from there?”
The difference is context. And it changes everything.
When you ask AI to teach you something without telling it who you are, what you already know, what tools you have, and what you’re actually trying to accomplish, it teaches generically. It gives you advice that is technically correct but often completely wrong for your specific situation. It tells you to press keys that don’t exist on your keyboard. It references features from a version you don’t have. It assumes a skill level that’s either too high or too low. And because it sounds confident the entire time, you don’t realize the advice doesn’t apply to you until you’ve already wasted an hour trying to follow instructions that were never going to work.
This is the foundation of AI Cred. The entire platform is built on this principle: you cannot teach someone effectively until you know where they’re starting from. AI Cred learns everything it can about a person (what they already know about AI, what tools they use, what they’re trying to accomplish, where the gaps in their understanding are) before it teaches them a single thing. People who go through AI Cred’s learning path extract more value than they get from a hundred generic AI courses, because every piece of instruction is driven by their personal context.
That shit matters. If you’re going to use AI to learn something new, front-load the context. Tell it everything relevant about your situation before you ask it to teach you anything. You’ll be stunned at the difference.
11. Research: Where All the Primitives Come Together (or Fall Apart)
This is the last section for a reason. Research is where every primitive in this guide either works together beautifully or collapses into an expensive waste of time.
Let me tell you about my early attempts at AI research, because they’re embarrassing and instructive.
When ChatGPT first launched their deep research feature, I was thrilled. Finally, a tool that could go out on the internet, search for information, synthesize it, and give me a comprehensive answer. So I did what any excited person would do: I told it to find me absolutely everything about a topic. Don’t hold back. Search everywhere. Give me everything.
It gave me 20-page essays that I never read.
Not once. Not a one-off failure. This happened repeatedly. I would get these massive documents back, skim the first page, and never look at them again. And then (this is the embarrassing part) when Claude launched their own deep research feature, I did the exact same thing. Gave the same prompts. Got the same massive outputs. And then, because I’m apparently a slow learner, I tried having one AI take both massive documents, combine all the knowledge, fact-check everything, and produce an even more comprehensive synthesis.
I now had two massive documents I was never going to read.
The problem was painfully obvious in hindsight: I broke every primitive in this guide. I didn’t define my output. I didn’t set goals or restraints. I didn’t have AI clarify what I was actually looking for. I gave it free rein over the entire topic, which is basically the definition of scope creep. I was directly causing context pollution by letting the AI dump everything it could find into a single conversation. I was doing the exact opposite of everything this guide teaches, and the results were exactly what you’d expect.
The fix is the same fix that runs through this entire guide: identify what you actually need to know before you start searching. Define the output. Set restraints. What question am I actually trying to answer? What format do I need the answer in? How much detail is enough? When am I done?
Research with AI isn’t about getting all the answers. It’s about pointing yourself in the right direction and understanding what you find.
And here’s something you might have noticed: I haven’t dedicated a single section of this guide to verifying AI’s output. No “always double-check the facts” section. No “AI hallucinates so be careful” warning. Why?
Because for everything else in this guide, you don’t need one. Look back at every primitive. You’re not asking AI to give you answers. You’re using AI to extract answers from yourself. You’re using it to clarify your own thinking, discover your own use cases, define your own outputs, enforce your own discipline. The AI is a thinking partner, not an oracle. You don’t need to fact-check your own thoughts.
Research is different. Research is the one place in this guide where AI is providing factual claims about the world. And that’s exactly where verification becomes critical. When AI tells you something about yourself and your own goals, you can evaluate that instantly because you’re the expert on you. When AI tells you something about external reality (a fact, a date, a statistic, a claim), that’s where you need to verify. Use AI to point you in the right direction. Then go confirm what it found. Check the sources. Read the primary material. You’re not using AI to give you the answers. You’re using AI to tell you where the answers are so you can find them yourself.
And I can’t stress enough how important this is. Don’t ask AI to tell you what the answers are. Ask AI to tell you where they are.
That distinction, using AI to think versus using AI to know, is the thread that runs through every single section of this guide. Get that right and everything else follows.
Where to Go From Here
Every primitive in this guide stacks on the one before it. You stop telling AI what to do and start letting it ask you questions. You discover what AI can actually do for your specific situation. You learn to define outputs you couldn’t have imagined. You use AI to clarify your thinking without letting it replace your thinking. You set goals and restraints and hold the line. You keep your conversations clean. You invest in the tool. You go deep instead of wide. You learn with context. You research with discipline.
None of this is complicated. All of it requires practice. And the people who are doing extraordinary things with AI right now? They’re not smarter than you. They’re not more technical. They’re not using secret prompts or special access. They’re just doing these fundamentals consistently, letting them compound, and building on them every single day.
The primitives are the point. Everything else is built on top of them.
If this guide helped you, share it with someone who’s just getting started. And if you’re ready to take your AI fluency seriously and figure out exactly where you stand and what to work on next, check out AI Cred. It’s the tool I built with my partner Nate specifically for this: understanding where you are, where the gaps are, and how to close them, all driven by your personal context.
Now stop reading and go practice something.
Nailed it!
Ahh so I’m not the only one running into folks bragging about using AI for free and then scoffing at how awful it is. The super weird part is they act as if they are super smart and the people using AI are being taking advantage of.