What If the Real Power of AI Is When It Gets Things Wrong?

Photo by Afif Ramdhasuma on Unsplash

We’ve become obsessed with AI accuracy.

Everywhere I look, people are testing large language models like ChatGPT or Claude or Gemini with a sort of anxious urgency:“Does it give me the exact right answer?”“Can it beat a human at this task?”“Will it write my article, automate my job, replace my analyst?”

But the more I work with AI—really work with it—the more I realise:We’re asking the wrong question.

Because what if the most valuable moments aren’t when AI gets it right…But when it doesn’t?

The Imperfect Prompt Is the Start of the Real Conversation

Let me be clear: I’m not excusing misinformation or errors.We should always strive for clarity, truth, and quality.

But if we expect AI to always give us precise, fully formed, plug-and-play answers, we’re setting ourselves up for disappointment—and worse, we’re missing out on what makes this technology different from anything we’ve seen before.

It’s not a search engine.It’s not a calculator.It’s not a vending machine for knowledge.

It’s something fuzzier. More fluid.A tool that responds to how we think, not just what we ask.

I’ve Learned More From “Wrong” AI Than Right Ones

Some of my most useful prompts have started with a response I didn’t expect.

Not because it nailed it.But because it didn’t—and I had to step back and ask:

• Why did it interpret my question that way?

• What assumption did I bake into the prompt?

• What would happen if I framed it differently?

That pause? That confusion? That "wait, no, that's not what I meant" moment?

That’s the start of learning—both for me and for the machine.

And it mirrors what we’ve always known to be true: growth comes from friction.In design, in dialogue, in relationships. And now, in prompts.

AI Is Built on the Internet’s DNA—Structure and Chaos

Here’s the thing most people don’t realise about LLMs:

They’re trained on structured and unstructured data.Not just clean spreadsheets or vetted encyclopedias.But Reddit threads. StackOverflow questions. Blog posts, TikTok transcripts, Wikipedia rabbit holes, Instagram captions, YouTube comments.

The good, the bad, the grammatically questionable.

It’s not a tidy world.But it’s ours—chaotic, diverse, wildly opinionated.

When you prompt an LLM, you're not querying a database. You're holding a mirror to collective human noise and trying to tune it into signal.

And that signal depends on your ability to:

• Ask clearly

• Iterate freely

• Listen actively

• Think critically

That’s not something you “get right” the first time.That’s a practice.

The Real Progress Is in the Practice

I bake. A lot.

And in baking, the first cake is rarely the best. Maybe the batter was too runny. Maybe I misjudged the heat. Maybe the crust looked right, but the center was underdone.

But each time I try again, I notice something:

• I stir more deliberately

• I measure more intuitively

• I tweak—not blindly, but based on what I’ve learned

That’s exactly what prompting AI feels like.

Your first result might be flat. Your second too dense.But with each round, it rises.

Why the Next Leap in AI Won’t Come from Better Data Alone

Yes, better data matters. But we’re nearing a point where more curated text from the internet won’t move the needle much.

The real value will come from:

• Our prompts

• Our corrections

• Our mistakes

• Our weird edge cases

• Our conversations that don’t go as planned

Unstructured, messy, human interactions.

That’s where the next learning frontier is.That’s where the refinement happens.

And it won’t come from passively using AI.It’ll come from building with it, pushing it, testing it—not just reading headlines and waiting to be wowed.

We’re All Still Forming

We act as if AI should already be perfect. As if ChatGPT should ace every test. As if AI tools should read our minds and give us brilliance on command.

Nothing Starts Perfect. Not your sourdough starter. Not your bench press form. Not your first time cleaning messy data for a dashboard.

Everything that holds its shape today began uneven, uncertain, and untested.

Growth isn’t something you download. It’s something that rises—bit by bit, test by test, batch by batch.

AI is no different.

I’m Building. And That’s How I Know.

At FYT, we’re not just teaching AI—we’re designing with it.We’re integrating it into workflows, building frameworks, and stress-testing its limitations with actual client data.

And I can tell you: AI is not a magic trick. But it is a multiplier.

The better your thinking, the better the results. The clearer your intention, the stronger the outcome.The more patient your iteration, the richer the insight.

It rewards curiosity more than correctness.

So What Should You Do?

Stop trying to be perfect. Start being present.

Treat the AI like a creative partner. Challenge it. Converse with it. Feed it your thinking—and let it reflect something back you didn’t expect.

That surprise? That moment of “not quite right”?

That’s the point.

Final Thought: Clarity Doesn’t Always Arrive. Sometimes You Bake It.

AI doesn’t always give us answers. But it gives us a process—a mirror, a practice, a provocation.

And if you let it, it can sharpen how you think, how you question, and how you learn.

Not because it’s always right.

But because, like you, it’s still rising.