AI Isn’t Software. It’s a Teammate That Needs Coaching

Coaching the Machine.

Why Context Engineering Will Redefine AI Power Users

We’re in a strange moment with AI. On one hand, it feels magical. Ask ChatGPT for a plan, an essay, or a pitch deck and you’ll get one back in seconds. On the other hand, we keep bumping into its limitations. Sometimes it hallucinates. Sometimes it fakes confidence. And sometimes it tells you to “check back in a few days,” like a confused intern trying to stall for time.

This gap between the promise of AI and its actual performance isn’t a failure of the tech. It’s a failure of how we use it. Too many of us still treat these systems like software. They’re not. They’re collaborators.

And that’s where a new discipline—context engineering—comes in.

From Prompts to Context

Early adopters obsessed over prompts: the magic phrases that supposedly unlock better answers. But a single clever sentence is not enough. “Write me a sales email” will always sound generic because you haven’t told the AI who you are, what you sound like, or what matters to your customer.

Context engineering flips this. It’s the art of giving AI all the materials a skilled colleague would need: your brand guidelines, customer transcripts, product specs, even your best past work. Layer in these ingredients, and the bland AI voice transforms into something that feels tailor-made.

Think of it less like spell-casting, more like briefing a new hire.

Why Coaching Beats Coding

Here’s the uncomfortable truth: the best AI users right now aren’t engineers. They’re coaches. Teachers. Managers. People who know how to extract great work from other humans are the ones who get the best results from machines.

AI will never tell you “no.” It’s too eager. It’s wired to please. That means it will happily make things up or tell you “great job!” even when you’ve missed the mark. If you don’t learn to push back—if you don’t learn to demand better—you’ll get output that flatters you but doesn’t serve you.

Good users give their AI a role. Be Dale Carnegie. Be a Russian Olympic judge. Be my most brutally honest colleague. This isn’t play-acting. It’s scaffolding. It tells the AI where to draw from its training, which associations to surface, and how to shape its response.

Techniques That Matter

Three simple techniques are already changing the way professionals work with AI:

• Chain of thought reasoning — Ask the model to walk through its logic before giving an answer. Suddenly, you’re not staring into a black box. You see the steps. You can question the assumptions. And the final result improves because the AI is forced to “think” more carefully.

• Few-shot prompting — Show, don’t tell. Feed it your greatest hits (and, if you’re smart, a bad miss too). Let the machine imitate your style, not the internet’s.

• Reverse prompting — Give it permission to ask questions first. Like a good teammate, it should stop guessing and start clarifying what it needs before it works.

Each of these nudges AI away from being a parrot and closer to being a partner.

The Flight Simulator for Leadership

One of the most exciting frontiers is roleplay. Imagine preparing for a tough negotiation by running it against three AI “windows”: one plays your adversary, one plays you, and one grades your performance. You can tweak the adversary to be more combative or more agreeable. You can replay the conversation until you find an approach that works.

It’s a flight simulator for leadership. And it’s only possible because AI can take on multiple perspectives at once—something humans struggle to do.

Expanding the Adjacent Possible

Every technology shift expands what’s possible. AI is no different. But the biggest constraint right now isn’t the models. It’s us. Too many of us haven’t learned how to brief, coach, or push these systems to their limits.

As more people master context engineering, the collective imagination expands. Ideas that once felt unthinkable suddenly move into reach. That’s the “adjacent possible”—the innovation frontier that grows as soon as you step toward it.

The real revolution won’t come from bigger models. It’ll come from better humans. The ones who learn to coach this strange new intelligence, to give it context, and to demand honesty instead of flattery.

Because AI may be “bad software,” but in the right hands, it’s very good people.