AI Hallucinations, Garbage In, and Who’s Really in the Driver’s Seat

"AI is a powerful tool—but only when you know how to use it."

You’ve probably heard people say:
“AI hallucinates.”
“AI makes things up.”
“AI can’t be trusted.”

But what do they really mean—and more importantly, how do we interact with AI to get the best results?

TL;DR

AI isn’t perfect—but it’s not “bad.” Hallucinations happen when humans provide vague, incomplete, or poorly structured instructions. You are the pilot; AI is the copilot. Be deliberate, logical, and verify results, and AI becomes a powerful tool that works for you, not the other way around.

What “Hallucinations” Really Are

When people talk about AI hallucinations, they mean the AI produced an output that sounds confident but is inaccurate, incomplete, or wrong.

Hallucinations can happen because:

• The data behind the AI is incomplete or biased
  
  

• Questions are vague or poorly structured
  
  

• The system is asked to infer beyond its training
  
  

Tip: Hallucinations happen at the intersection of data, logic, and human input. Garbage in, garbage out still applies.

Example: You ask AI for a historical fact without specifying a time or region. It might give a confident-sounding answer that’s only partially correct—or completely wrong.

You’re the Pilot, AI Is the Copilot

Think of AI as a copilot. You’re still the pilot.

A perfect example: in The Office, Michael Scott blindly follows GPS instructions and drives into a pond. He’s technically “driving,” but he’s not thinking critically—he’s treating the system as infallible.

AI works the same way. Treat it as a tool, not an authority, and you’ll see far better results.

Real-world use cases where AI works as a copilot:

• Writing drafts or summaries for reports

• Assisting in coding by suggesting logic or debugging errors

• Researching trends, then letting you verify and refine the findings
  
  

The Peanut Butter & Jelly Problem

Early computer science classes often use a simple exercise: writing pseudocode for making a peanut butter and jelly sandwich. Most students get it wrong.

“Get jelly. Put it on bread.”

Too vague. Computers—and AI—need explicit, sequential instructions:

1. Get the jar

2. Open the jar

3. Get a knife

4. Take jelly

5. Apply jelly to bread, specifying orientation
   
   

Miss a step, and the process fails. AI works the same way. If your prompt is vague, your output will be too.

Logic Beats Emotion

Machines don’t have feelings. They don’t sense frustration, sarcasm, or tone unless you define it.

If your inputs are careless or emotional, the output reflects that. AI is a mirror of your approach.

Being methodical and precise produces better results than vague, emotional, or hurried prompts.

Binary Systems, Human Expectations

AI runs on binary logic—ones and zeros. Absolute. Structured.

Yet people often expect it to act as a perfect oracle. That’s unrealistic, even for humans. You wouldn’t blindly accept a stranger’s historical claim—but people do exactly that with AI. Verification and independent thinking remain essential.

Minimizing Hallucinations

You can reduce AI errors by:

• Asking clear, structured questions

• Being specific about your intent

• Verifying outputs independently

• Interacting ethically and responsibly
  
  

The better your inputs, the better your outputs.

Technology Is a Tool, Not a Master

We live in a world saturated with technology. Kids in strollers swipe through devices before they can even speak. That shows how important digital literacy really is.

If you don’t understand technology, you don’t control it—you follow it. But with AI, you can be the pilot. You guide the tool. You make decisions. AI works for you, not the other way around.

Bottom line: AI is not negative. It’s a powerful, practical tool—when used thoughtfully. Its effectiveness depends on the care, logic, and awareness of the person holding the controls.

Author’s Note

This article was written by Douglas E. Fessler. The ideas and reflections are my own, drawing on decades of experience in IT, environmental monitoring, STEM education, and community initiatives. AI-assisted tools were used to structure and clarify complex concepts — a reflection, in itself, of the subject explored.