Why Misunderstanding AI Fuels Fear..
What "hallucination" and “learning” really mean!
Last week, someone said this in a meeting:
“We can’t use that tool. What if it hallucinates?”
Heads nodded.
No one asked what hallucination actually meant.
And that’s when I realized - we are making decisions about AI based on words we don’t fully understand.
There are two sentences I hear all the time in conversations about AI:
“AI hallucinates.”
“AI keeps learning.”
They sound like two separate problems. In fact the latter does not even sound like a problem but it creates immense fear and anxiety in people’s minds.
These two things are two sides of the same misunderstanding.
In HR, that misunderstanding shows up fast - in AI governance debates, adoption resistance etc.
But once you understand what’s really going on, you kill both birds with one stone.
So let’s name the birds first.
🐦 Bird #1: Hallucination
Let’s keep this simple.
Hallucination is when AI gives you a wrong answer with the same confidence it would give you a right one.
That’s it.
If I answer a question and I’m unsure, you’ll probably hear it in my voice:
“I think…”
“I’m not 100% sure…”
“Let me double-check that.”
AI doesn’t do that.
It doesn’t hesitate.
It doesn’t say “hm.”
It just answers very confidently even when it’s wrong.
That confidence is the problem.
Why does “hallucination” happen?
Not because AI is broken.
Not because it’s malicious.
And not because it’s “making things up.”
It happens because AI models like ChatGPT and their kind were trained on a large snapshot of information from the past.
Think of it like this:
AI studied really hard for an exam.
Then the exam pattern changed.
Some facts it learned:
Were true once
Were true in a different context
Or were true for most cases, but not this one
When you ask it a question and it doesn’t have the exact answer, it does what it was trained to do:
👉 Predict the most likely answer based on patterns.
Sometimes that prediction is wrong but the confidence stays the same.
That’s hallucination.
“But all AI hallucinates!”
Almost.
Here’s the important nuance:
All “Large Language Models (LLM’s) hallucinate.
So any software product or tool you have built (such as a chatbot) that relies on a large language model tends to hallucinate.
But not all AI tools are large language models.
If a tool:
Follows rules
Pulls information from a database based on your inputs
Executes workflows
It’s not hallucinating - it’s just executing logic.
Hallucination only shows up where “generation” happens.
So how do we reduce hallucination?
By giving AI tools better, more specific, and more current context.
Let’s use a simple example.
If I ask:
“What’s the capital of France?”
And AI confidently says:
“London.”
That’s hallucination.
Now I ask:
“What’s the capital of France, and by the way, check the atlas before answering.”
Suddenly, AI has something concrete to refer to.
That’s what:
Web search
Uploaded documents
RAG systems
Internal knowledge bases
actually do.
Examples of these features in ChatGPT:
They don’t make AI smarter.
They give it something real to look at.
Does this eliminate hallucination completely?
No.
AI can still get confused if you give too much context or messy context.
So hallucination never goes to zero.
But it drops significantly when context is relevant, current, and scoped.
Giving clear instructions by way of prompting helps as well.
🐦 Bird #2: “AI keeps learning”
This myth causes even more anxiety.
People hear:
“AI learns over time on its own”
And imagine:
It’s absorbing everything
Updating itself constantly
Getting smarter with every conversation
That’s NOT what’s happening.
Most AI models are NOT constantly learning
The models we use day to day were trained:
Months ago
Sometimes years ago
And their knowledge is frozen at a certain point in time
They don’t update themselves just because you talked to them.
“But what about search mode?”
Great example.
When you turn on search mode in ChatGPT, it looks like AI is learning everything on the internet.
What’s actually happening is simpler:
You ask a question
AI creates a search query (like Google would)
It fetches search results
It reads them
Then it answers
The model didn’t learn anything new.
It just looked something up.
Same pattern. Same solution.
What about personal or company-specific questions?
This is where things get risky.
If you ask AI about:
Your company policies
Your financial situation
And you don’t provide context, AI will still answer.
Confidently.
Generally.
Incorrectly.
Not because it knows you but because it knows patterns.
To make AI “learn” you have to:
Provide that context
Or allow it to maintain it over time
This is where memory comes in:
Think of ChatGPT’s memory feature as a notepad.
Every now and then, it writes down:
Your preferences
Your role
How you like things explained
You can:
View it
Edit it
Delete it entirely
The model didn’t change.
The brain didn’t rewire.
AI just got better notes.
And better notes = better answers.
So what’s the one stone that kills both birds?
“Hallucination” happens when AI lacks the right context.
“Learning” happens when AI gains more context.
Same mechanism.
Different symptoms.
AI doesn’t magically become smarter.
It becomes better informed.
Final thought
AI isn’t confident because it’s correct.
It’s confident because it always sounds confident.
Your job, especially in HR, isn’t to eliminate that confidence.
It’s to:
Know when AI needs more context
Decide what context it should have
And design systems that don’t confuse confidence with truth
Once you understand that…
Hallucination stops feeling scary.
Learning stops feeling mysterious.
And AI becomes what it was always meant to be:
A tool and NOT a mind.
Stay curious 🙂
AI Lady
About the Author
I’m Priya Tahiliani, and I’ve spent the last 15 years at the intersection of HR and Technology. My career has centered on SAP HCM and SAP SuccessFactors consulting, working with Big Four firms and clients worldwide.
I led various AI adoption initiatives, developed and launched my company’s first AI tool by building a strong cross-functional partnership with IT, and I continue to collaborate with HR leaders to shape the future of work through AI.
Beyond work, I serve as Vice President of Public Relations at Toastmasters. I’m also the Founder of the AI Collective – Oakville Chapter in Canada, which is the world’s largest community for AI professionals - a network dedicated to learning and leading responsibly with AI.
And of course, I write the AI Lady newsletter, where I share my experiences, insights, and thoughts about how AI is reshaping our workplaces.
My newest hobby - learning how to produce music and remixing songs!
Feel free to comment if you want to listen to my latest creation :)