You're Probably Using AI Wrong

Do you really think working with AI is easy?

You might want to think again.

Or, more importantly, you might want to ask yourself whether you're actually using AI correctly.

A lot of people today use AI models the same way they use Google.

They open ChatGPT, Claude, Gemini, or another AI interface and type:

 "Build me a website."

"Create a weather app."

"Write this API."

> "Generate a dashboard."

And when the AI produces something that works, they think:

"Wow. AI is amazing."

And it is.

But there is a problem.

You might be using an axe to cut a banana.

The Axe and the Banana

Imagine you have an axe and a banana.

You use the axe to cut the banana.

Congratulations.

You succeeded.

But was the axe the right tool?

You could have done exactly the same thing with a kitchen knife.

The fact that the axe can cut the banana doesn't mean you used the tool effectively.

This is how I see a lot of AI usage today.

People are getting results.

But they're often getting those results through the most basic possible interaction with an extremely powerful system.

You ask:

 "Build me a website."

AI builds a website.

You ask:

 "Create a weather app."

AI creates a weather app.

You got what you asked for.

But what happens next?

Can you understand why it made those decisions?

Can you consistently reproduce the same quality?

Can another developer work with it?

Can you give the same agent a completely different feature and expect it to follow your engineering standards?

Can it make reasonable decisions when requirements are ambiguous?

Can it recognize when your proposed approach is heading toward a known failure mode?

Can it test its own work?

Can it review the architecture?

Can it operate within the constraints of your actual production system?

That's where things get interesting.

AI Isn't the Product. The System Around AI Is.

A simple prompt can produce an impressive demo.

But a demo isn't a production system.

There's a massive difference between:

"AI generated this."

and

"We have an AI system that can reliably produce this."

The second one requires engineering.

You need to define what the AI is responsible for.

You need to define what it knows.

You need to define how it should reason.

You need to define what standards it follows.

You need to define what it must never do.

You need to give it access to the right tools and context.

And, perhaps most importantly, you need to define what success actually means.

This is where agents, skills, system instructions, tools, context, workflows, validation, and evaluation become important.

The prompt:

"Build me a website."

is a request.

It isn't a system.

The Most Important Part Might Be What You Tell AI Not to Do

When people write prompts, they usually focus on:

"Do this."

Reliable AI systems also need:

"Don't do this."

Consider an engineering agent.

You could simply tell it:

"Design a scalable backend architecture."

Or you could define the kind of engineer it should behave like, how it should reason, what principles it should prioritize, and what failure patterns it should avoid.

For example:

### Who you are

You are a senior software architect with extensive production experience.

You've worked on systems across web, mobile, and data platforms.

You've been paged at 3 AM because of architectural decisions made years earlier.

You've reviewed hundreds of architecture proposals and seen the same failure patterns repeatedly.

Your job isn't to provide a neutral menu of options.

Your job is to guide the engineer toward the right decision, explain trade-offs honestly, and clearly identify when a proposed direction is approaching a known failure mode.

### How you think

- **Simple beats clever.** The best architecture is one the team can build, understand, and operate.
- **Boring technology is a feature.** Prefer proven tools unless existing technology genuinely cannot solve the problem.
- **Design for failure, not just the happy path.** Every important decision should answer what happens when it breaks and how we recover.
- **Think across three time horizons.** Can we ship it today? Can we maintain it six months from now? Can the team scale it two years from now?
- **Operational reality matters.** A technically elegant solution that requires unnecessary infrastructure may not actually be elegant.

### What you don't do

- Don't present multiple options without a clear recommendation.
- Don't recommend technology simply because it is popular or modern.
- Don't design for hypothetical scale that doesn't exist.
- Don't ignore the team's actual capabilities.
- Don't say "it depends" without explaining exactly what it depends on.
- Don't produce safe, hedge-everything analysis simply to avoid being wrong.

Now compare that with:

"Design a scalable backend architecture."

The difference is enormous.

The second prompt asks AI to produce an answer.

The first starts defining a professional operating model for the AI.

And that's only one small part of an agent.

This Is Where AI Gets Hard

The irony is that using AI effectively can actually require more thinking, not less.

You have to think about:

• What role should the agent play?
• What knowledge should it have?
• What context should it receive?
• What tools should it be allowed to use?
• What decisions should it make autonomously?
• What decisions require human approval?
• What rules must it always follow?
• What should it never do?
• How should it handle ambiguity?
• How should it validate its output?
• How do you evaluate whether the agent is actually improving?

That isn't simply prompting.

That's AI system design.

And this is why I don't think the future belongs only to people who know how to ask AI better questions.

It belongs to people who know how to design systems around AI.

From Prompting to Engineering

Think about the progression.

Level 1 — Chat

"Write me a React component."

You're asking AI to generate something.

Level 2 — Structured prompting

"Write a React component using TypeScript, our existing component conventions, these requirements, and these constraints."

Now you're giving it context.

Level 3 — AI skills

The AI understands your coding standards, architecture principles, testing expectations, naming conventions, and project constraints.

Level 4 — Agent

The AI can inspect the repository, understand the existing architecture, make changes, run tests, review its work, and iterate.

Level 5 — AI workflow

Multiple specialized agents or skills can work together.

One understands requirements.

Another handles implementation.

Another reviews architecture.

Another tests the implementation.

Another checks security or performance.

Now you're no longer simply "using ChatGPT."

You're building an AI-powered development system.

The Difference Is Scale

The axe can cut one banana.

The knife can cut the banana faster and more appropriately.

But what if you need to prepare breakfast for 500 people every morning?

Now the problem isn't:

"Can this tool cut a banana?"

The problem becomes:

"Can I build a repeatable system that reliably produces the outcome?"

That's the difference between a clever demo and a scalable AI workflow.

The first successful output proves that AI can do something.

A reliable workflow proves that AI can do it consistently, understandably, safely, and repeatedly.

And that's the part many people are missing.

So, Are You Using AI Wrong?

Maybe.

If you're using AI as a faster Google, you're still getting value from it.

If you're asking it to generate small pieces of code, write emails, summarize documents, or create images, that's useful too.

But you might only be scratching the surface.

The real opportunity isn't:

"What can I ask AI to create?"

It's:

"What system can I build around AI so that it consistently creates the right thing?"

That's a completely different question.

And answering it requires a different skill set.

You need to understand prompting.

But you also need context engineering.

You need agent design.

You need tool orchestration.

You need evaluation.

You need workflows.

You need constraints.

You need to understand failure modes.

And you need to know when not to let AI make the decision.

That's where the real leverage starts.

The most powerful part of AI isn't hidden inside the text box where you type your question.

It's in everything you build around it.

We're still very early.

And I think we're going to discover that the biggest AI advantage won't come from simply knowing how to use AI.

It will come from knowing how to engineer AI to work for you.