What are AI “hallucinations”?

Your chatbot just gave a customer a fake refund policy. Cool cool cool.

Your AI-generated blog post just cited a journal that doesn’t exist. Sweet.

Or your sales assistant AI just made up a feature your product’s never had. Cheers.

Welcome to the weird, slightly terrifying world of AI hallucinations—where the machines sound confident but don’t always tell the truth.

And no, it’s not because they’re broken. Or evil. Or plotting against humanity in your CRM.

They’re just really, really good at guessing.

So... what the hell is an AI hallucination?

In plain English? An AI hallucination is when an AI tool says something that’s flat-out wrong, but says it so convincingly you might believe it.

We're talking:

• Totally made-up citations
• Fabricated stories or "facts"
• Gibberish that sounds right until you look closer
• Code that compiles, but does nothing

Why “hallucination”? Because like a person tripping on mushrooms, the AI thinks it sees something—but it’s not real. And it isn’t lying on purpose. It just doesn't really... know things. It generates based on patterns, not truth.

That’s terrifying if you’re in a regulated industry. Or, you know, have customers.

Why does this even happen?

Big reason #1: Garbage or gappy data

AI models are trained on internet-sized haystacks of content. When that training set is riddled with outdated info, contradicting sources, or flat missing in areas—it’ll guess to fill the gaps. Not great when accuracy matters.

Big reason #2: The models are too dang big

Large Language Models (LLMs) are incredibly complex. Like, Nobel-prize-student-on-six-energy-drinks complex. Sometimes they overinterpret patterns and go rogue—just like your over-caffeinated head of strategy pitching a rebrand at 1am.

Big reason #3: Crappy prompts

What you put in matters. Vague, open-ended, or poorly structured prompts can send your AI spinning into hallucination mode as it scrambles to generate "something." Anything. Whether it’s true or not.

Big reason #4: Decoding errors

Yeah, this one gets geeky fast. But here’s the short: AI predicts words one token at a time. Each word influences what comes next. So if it gets off track early? The error snowballs. One fake stat becomes a whole fake paragraph before you can say, “please cite your source.”

Why should I care?

If you’re building systems that run without human oversight (think 24/7 lead gen, automated customer support, AI-written proposals), hallucinations can cost you real money and credibility.

Here’s what’s at stake:

• Bad decisions: Imagine a financial forecasting tool generating numbers that aren’t real. Your ops guy builds the Q3 plan around it. Six figures gone. Whoops.
• Trust erosion: Customers notice when your support chatbot gives them conflicting policies or links that 404.
• Compliance headaches: Regulated sectors (healthcare, law, finance) can’t afford "brain farts" from AI that invents advice. Risk of penalties is real.
• Wasted time: Your team spends hours double-checking AI output—defeating the whole point of automating it in the first place.
• Brand embarrassment: Remember Microsoft’s Tay chatbot going rogue on Twitter in under 24 hours? Hallucinations helped take it down in flames. Not a press cycle you want on your plate.

Fun stat: Industry estimates suggest hallucinated outputs are behind millions in inefficiencies every year—especially in credit evaluation, legal writing, and customer support. It’s a known problem.

Okay, but is this the same thing as bias?

Nope. Bias is when a system reflects skewed perspectives baked into its training data (like discrimination or unfair treatment).

Hallucination is just the AI making stuff up—even if the data was squeaky clean. It’s not malicious. It’s more... cluelessly confident.

Think of bias as a preloaded worldview. Hallucination is drunk improv.

How to keep your AI systems from hallucinating (as much)

First things first: You can’t eliminate hallucinations completely. But you can design systems that manage them smartly.

Here’s what actually works:

1. Engineer better prompts

Sounds basic, but it’s everything. Prompt engineering is half science, half art. Clear, structured inputs dramatically reduce hallucinations—especially in business contexts.

Instead of: “Write a business case for our CRM.”
Try: “Write a 500-word business case for switching to our CRM, focused on lead tracking and pipeline reporting. Use real customer pain points and include 3 statistics from verifiable sources.”

2. Keep humans in the loop

Don’t set it and forget it. Especially for client-facing tasks.

Have your marketing manager QA the AI’s LinkedIn copy. Let a paralegal review AI-drafted intake scripts. Pair AI with people who can catch the goofs without burning their whole day on it.

3. Improve your training/use data

Some AI workflows work better when you fine-tune the model with your own trusted, up-to-date data—especially if you’re in a niche or technical field (think: healthcare compliance, B2B SaaS integrations, procurement systems).

Not always quick. Definitely worth it for long-term accuracy.

4. Use evaluation benchmarks

Seriously—treat your AI like a junior employee. Test it before you let it fly solo.

Have criteria: What counts as accurate? What gets flagged for review? Build monitoring tools or checkpoints before outputs hit customer inboxes.

5. Don’t just rely on

River Braun, founder of Timebender, is an AI consultant and systems strategist with over a decade of experience helping service-based businesses streamline operations, automate marketing, and scale sustainably. With a background in business law and digital marketing, River blends strategic insight with practical tools—empowering small teams and solopreneurs to reclaim their time and grow without burnout.