Hallucinations are a feature not a bug

One of the biggest concerns surrounding AI is its tendency to hallucinate. But what exactly is an AI hallucination and where do we think they come from?

At its core, a hallucination is just this:

The model gives you something that sounds right, but objectively isn't.

Fake citations. Made-up facts. Technical nonsense wrapped in a confident tone. Hallucinations can take shape in many forms. They look polished, read smoothly but are wrong. The confidence behind them often makes them difficult to spot as well.

The mistake is thinking these hallucination are a bug that engineers just haven't cleaned up yet.

It's not.

Hallucination is actually baked into how these systems work.

LLMs don't actually "know" the truth. They predict the next likely word in a sequence. And when the data is thin, the question is vague, or the prompt demands certainty… the model doesn't pause to reflect. It does what it was trained to do, complete the pattern and provide a response. In the past I've described this behaviour like a heat seeking missile, determined to hit its target no matter what.

But, hallucinations also can feel strangely human. People do this too. We confidently guess, fill in memory gaps, and retell stories with details that weren't there.

AI just does it at scale, with perfect grammar and zero shame.

So, can you eliminate hallucination completely?

No. Not with today's probabilistic models. But you can try to reduce them.

How?

• Give it strong, precise context.
• Let it express uncertainty instead of forcing a yes/no answer.
• Ground it in verifiable sources and make it say when it can't.
• Break big questions into smaller ones so it isn't forced to synthesize everything in one shot.
• And when the stakes are high: cross-check. Models disagree in interesting ways.