Video: How to Make AI Smarter Than Your Smartest Employee

Good morning.

Got another video for you in this issue.

This time, it’s about leveraging AI models to be your “smartest” team member.

Or to just be “smarter” than you.

Let’s go.

— Sam

IN TODAY’S ISSUE 🤖 

• Why most people underuse AI

• The $244B AI market & hidden potential

• Step 1: Prompt & Context Engineering

• Context stacking done right

• Step 2: Giving AI your data & style

• Building a digital twin with AI

• The mistake of not integrating AI

• Step 3: Deploy AI into your workflows

• AI as a growth engine in business

• Smarter use of AI = your best hire

Let’s get into it.

How to Make AI Smarter Than Your Smartest Employee

If you’re more of a reader:  

GPT-5 was trained on over 1 trillion words.

It took millions of GPU hours and cost OpenAI over $100 million to do it.

But most people are still barely scratching the surface with it.

AI feels like it’s everywhere now, all of your friends are using it…

And most still treat it like a junior assistant that’s good for speed and nothing else.

But in reality? It can be smarter than your smartest team member.

You see, AI might be limited, but I guarantee you haven’t come close to reaching those limits.

There’s a reason the artificial intelligence market is worth around $244 billion.

I know this because I’ve built AI systems for Fortune 1000 companies and online entrepreneurs for years. I know how advanced the models are.

But if you don’t prompt like a strategist, don’t expect the AI to respond like one.

So by the end of this, I’ll show you the three steps to make ANY AI model perform better than the best people on your team.

Most people give ChatGPT paragraphs of background, format, objectives, and even examples. Then they wonder why the output is robotic, or just completely wrong.

That’s not the AI’s fault. That’s yours.

Why? Because most of your prompts don’t have the structure that I’m going to reveal.

AI is like a high-performing team member. In fact, it could be smarter than your smartest member only if you know how to give it the right direction.

You wouldn’t hire a copywriter and say, “Hey, just write something.” You’d give them a clear brief. You’d tell them the purpose, and the structure.

Even GPT-4, GPT-5 or Claude 4 becomes “dumb” if you ask it dumb questions. But it becomes brilliant when you feed it brilliance in the first place. Therefore, your first step is mastering prompt engineering to direct the AI’s mind.

Prompt engineering is the process of giving effective prompts or inputs to AI models so you can get your desired output. It tells the model what to ignore and what to focus on.

The first step of prompt engineering is using constraints. A constraint is a limit you give to AI to make its answer more useful. It’s a way to tell the AI: ‘’Stay inside this box. Only do this.’’

When you use constraints, you make your AI sound smarter. You stop getting generic fluff, and only get specific, high-quality outputs that match your needs. Constraints also force the AI to prioritize, which means it puts more effort into what matters most.

Imagine asking someone to cook dinner and they make a five-course meal when all you wanted was a quick sandwich. That’s what happens when you use AI without a constraint.

But if you say, “Make me something with no dairy, ready in 10 minutes, and under 500 calories,” now they know what you actually need.

This is why it is so important to add constraints when using AI. The best way to add constraints is by starting with a set of rules.

Tell AI your specific guidelines, what structure you want to avoid, and what format you want to follow.

A simple example would be something like this: “Write this in under 100 words. Use simple language. Avoid buzzwords like ‘cutting-edge’ or ‘synergy.’ Use second person (‘you’) and short, punchy sentences.”

Now, AI knows your limits and boundaries, and that’s how it becomes smarter than your smartest employee.

After this, you need to use context stacking with AI. This is where most people drop the ball. Context stacking means feeding the AI layers of helpful background before giving it a task.

The background information should tell AI what you want, why you want it, and who you want it for.

If you have a blog post or customer avatar, include it in the prompt. Say, “Based on this landing page…” or “In the tone of this brand voice guide…” The more context it has, the smarter it gets.

Let’s say you're building a productized agency and want GPT to audit your competitor's funnel. Here’s how an average user gives a prompt: “Analyze this landing page and rewrite it.”

But if you use context stacking, you’ll give a prompt like this:

“Here is a landing page from my competitor. This brand targets 7-figure coaches but their copy uses general business language. Identify 3 positioning flaws using the lens of customer specificity, and differentiation. Then suggest how I can reverse each flaw into an asset for my own offer, which helps the same audience build backend systems.”

Finally, prompt it to critique itself. Once it gives you an answer, follow up with: “How could this be more emotionally resonant for an overwhelmed founder?”

You’ll be shocked at how good it gets at fixing its own work, when you prompt it like a coach.

But even then, you're still only halfway there.

Because no matter how good your prompts are, you're still speaking to a model trained on everyone else’s data which means you're going to keep getting outputs that sound like everyone else.

Once you know how to give the right prompts, your AI feels better, but not great. It’s still missing something. The writing doesn’t quite sound like you. The logic doesn’t follow your frameworks. You’re still getting generic ideas.

But it’s not a prompt issue anymore, you just haven’t trained your AI enough. If you want AI to truly think like you, and make decisions like you, you need to train it using your own frameworks.

Because general models are trained on everyone’s data instead of your specific business, brand, or logic.

Most tools, whether it's Jasper, Notion AI, or even your fancy $100/month writing platform, are all pulling from a common base model. They’ve been trained on Reddit threads, Wikipedia articles, corporate blogs, and open web text.

This is because there’s much more average content on the internet than there is excellent content, and AI is overwhelmingly trained on all the average stuff.

Think about it. The web is filled with blog posts no one reads, clickbait headlines, and common ideas recycled over and over again. That’s the ocean ChatGPT swims in. So when it generates your answer, it pulls from that huge pool of "generality.’’

So, when you get a mediocre answer, it’s not because the model is broken. It’s because almost all AI models are predicting the most likely next word based on millions of other average sentences they have seen. They are blending together common ideas that sound reasonable, but have no edge.

They do not know your sales page content, your tone, or your unique way of explaining things. That’s why everything they generate sounds too familiar. Because your competitors are using the same AI, with the same prompts.

That’s where the real risk begins. If everyone uses the same brain, then nobody stands out. Your message sounds and reads and feels like everybody else’s. It might be grammatically perfect but strategically it falls flat. So, the second step is training your AI using your own content and systems.

You don’t need to fine-tune a base model from scratch. You just need to give the AI what it’s missing: your unique touch.

Start with document embedding. This means uploading your sales pages, client emails, SOPs, and even your past content into tools that let AI “read” and reference them. Think of it like giving the AI a memory bank filled with your knowledge.

Inside ChatGPT, you can use Custom GPTs, they let you upload files and instructions so your AI acts like a trained assistant who’s read your playbook.

If you want even more control, use vector databases, tools like Pinecone or Weaviate, to build searchable knowledge systems the AI can tap into live.

Next, give it your style. This is your way of phrasing things, how you write hooks, what kind of metaphors you use, and how you structure your arguments. Don’t just tell the AI what to do. Show it how you do it.

Then, train it on your thinking. Start by uploading your foundational documents…sales pages, long-form emails, SOPs, and coaching frameworks. Feed it the content you’d give to a new hire on day one. This gives the AI raw material to study your logic, language, and flow.

Then move beyond words, and teach it your reasoning models. For example, if you always structure persuasive arguments as: Problem → Reframe → Logic → Belief Shift → Offer… encode that as a reusable prompt pattern. Say: “Always structure my message in this flow.”

Next, show AI how you resolve objections, how you break down abstract value, and how you make positioning decisions. Walk it through these steps using examples, feedback loops, and improvement prompts.

At this point, your AI is thinking like you, making decisions like you. You’ve taken a generic model, and turned it into a strategic clone of your voice. But this is where most people make the biggest mistake.

They build their AI, and then leave it open in a browser tab, writing occasional emails or blog posts. That’s it. You’ve trained a digital brain to think like you, and you’re using it like a note-taker?

The real problem is most users never plug AI into their actual business. It stays isolated, and that’s why it never scales. Because without integration, your AI doesn’t drive growth. It just writes content.

Smart AI can become your business operator. It can become your COO, your copy chief, your research analyst, your onboarding specialist. It can draft, respond, route, analyze, and execute, but only if you let it.

Right now, most people don’t understand this. They’re still stuck in chat windows, wondering why they feel busy but aren’t growing. So, the final step is deploying your AI across your actual business engine.

This is because true AI leverage comes from integration into real workflows. That means using AI to trigger tasks, generate assets, make decisions, and analyze data.

Start by mapping where you already work, your CRM, your Notion, your Airtable, your Slack, your marketing tools.

Ask: “Where am I doing the same tasks over and over?” That’s where AI belongs.

Use AI to generate assets: sales emails, SOPs, client responses, funnel drafts. Then use automation tools like Zapier, Make, or n8n to plug those assets directly into your systems.

For example: When a new lead fills out your form, AI drafts a personalized follow-up email, and triggers a sequence in your email platform. You do not need any human input, you just need the right execution.

Next, give AI real data. Connect it to analytics dashboards. Feed it conversion metrics, sales calls, support logs. Let it generate insights so it can create a weekly report that says: “Customer churn increased 12% last week. Here are 3 possible causes based on sentiment in support tickets.” That’s the real intelligence.

Then deploy it in your support and ops pipelines. Use AI to triage incoming requests, draft thoughtful replies, escalate emotional cases, and log resolutions. Train it on your FAQ, your tone, and now your support is fast and humanized.

And finally, give your AI a dashboard. A hub where it sees everything, including your tasks, your clients, your campaigns, projects, metrics and results so it can report back like a Chief of Staff. That’s when AI becomes strategic.

When you’ve got that, you’ve got something most people never will.

That’s when you’ll get not a smarter AI, but a smarter relationship with AI because when AI has context and action, it becomes a growth engine.

Look, you don’t need a smarter AI, you need to be smarter with AI because it’s not about better tech, it’s about better thinking, better communication, and better systems.

When you treat it like a strategic partner, it becomes the most valuable hire your business has ever made.

This is how you build companies that run faster and grow on autopilot.

A lot of people will bristle when people say AI is “smart” or “intelligent”.

That’s fine, I get it. These models aren’t smart or intelligent in the same way humans are. They don’t have a “brain” to begin with.

But when you’ve seen these models produce outputs and actions that are basically what a human would produce and do…

Then there’s something going on “under the hood”.

Smart, intelligent—who cares?

Whatever we end up calling it, I encourage you to focus on using all this to your advantage in your business.

Until next time,
Sam Woods
The Editor

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