Hallucinations Aren’t Errors - They're Misaligned Intent
Every time the topic of AI hallucinations comes up, someone calls them “errors.” That framing sounds right, but it’s not.
To a human, a hallucination means perceiving something that isn’t there. It’s an error of the mind. But for a large language model (LLM), a hallucination isn’t a malfunction. It’s a natural outcome of doing exactly what it was built to do: generate the next most probable word based on the data and context it was given.
In other words, what we call a mistake might actually be the system performing perfectly within its own rules.
Coherence vs. Correctness
Humans measure truth by how well a statement aligns with reality. Models measure success by how coherent their next token is with the previous ones.
When an LLM “hallucinates,” it’s not breaking a rule — it’s following one. It’s optimizing for linguistic probability, not factual accuracy. The gap between those two is where the hallucination lives.
Think of it this way: the model doesn’t retrieve knowledge from a database. It constructs knowledge based on patterns it learned during training. What appears to be a confidently stated fact is, in reality, a beautifully composed guess.
That’s not a bug. It’s the core design.
The Source of Hallucination
Inside the model, meaning isn’t stored as facts but as relationships — vast webs of semantic proximity between words, ideas, and contexts. When prompted, the model navigates this internal geometry to find the most probable continuation of a thought.
Most of the time, this works astonishingly well. But occasionally, that semantic navigation drifts: the model chooses a path that sounds right in language space yet doesn’t exist in the real world. That’s semantic drift — the root of hallucination inside an LLM.
It’s not the model “making something up.” It’s the model following a pattern that statistically fits, even when that pattern misrepresents truth.
Why We Call It an Error
Humans and machines don’t share the same objective functions. When you ask a model a question, you want truth or relevance. When it answers, it wants fluency and coherence.
Those goals only align some of the time. The rest of the time, we see hallucination — not because the model is broken, but because our intent wasn’t fully expressed in a way the model could follow.
We think the model misunderstood us. In reality, we didn’t specify the contract clearly enough.
The Real Problem: Misaligned Intent
Hallucinations are a mirror. They reflect our assumptions about how intelligence should behave.
An LLM doesn’t “know” what you mean — it predicts what people like you tend to mean when using similar words. That’s a subtle but powerful distinction. When its learned context doesn’t match your mental model, it fills the gap with probability — not truth.
So when leaders talk about “eliminating hallucinations,” what they’re really talking about is intent alignment: designing systems where human purpose is encoded as clearly as possible in the model’s context.
What This Means for Business Leaders
If you’re deploying AI in your organization, don’t dismiss hallucinations as simple errors to be “fixed.” Treat them as symptoms of unclear communication between human and machine.
Every hallucination is data — evidence of where intent and design diverge. The more we study those moments, the closer we get to systems that truly understand context, not just language.
In the future, the best AI systems won’t just predict text; they’ll interpret intent. And the best organizations will be the ones that design for that difference, instead of denying it.
Takeaway: A hallucination isn’t a system failure — it’s a signal that alignment still has room to improve. The fix isn’t better syntax. It’s better intent design.
Coming Next — Part 2
When Hallucinations Have Context: RAG’s Fragile Anchor How retrieval-augmented systems shift hallucinations from language to data — and what that means for trust, governance, and system design.
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