What AI Hallucinations Actually Are — and What They Aren’t

What people usually mean by “hallucinations”

When people say an AI is “hallucinating,” they usually mean one thing:
the model produced information that sounds plausible but is false.

That might be:

• a made-up citation
• an invented technical detail
• a confident answer to a question it does not actually have evidence for

The key issue is not tone or creativity.
It’s false certainty.

What hallucinations actually are

At a technical level, hallucinations are the result of probabilistic language generation.

A language model does not retrieve facts.
It predicts the most likely next token based on:

• training patterns
• the current prompt
• the surrounding context

If the model lacks reliable signal, it does not stop.
It continues predicting anyway.

That continuation — when unsupported by verifiable information — is what we call a hallucination.

Importantly:

• the model is not “imagining”
• it is not accessing hidden memories
• it is not lying

It is doing exactly what it was trained to do.

What hallucinations are not

Hallucinations are often misunderstood as evidence of deeper or darker behaviour. They are not:

• Hidden memory recall
  The model is not pulling secret facts from storage.
• Private data leakage
  Hallucinations do not indicate access to user conversations or logs.
• Independent reasoning
  The model is not “thinking for itself” in the human sense.
• System intent or deception
  There is no goal to mislead. Only continuation.

The confidence comes from language fluency — not from knowledge.

Why hallucinations feel convincing

Hallucinations are especially persuasive because:

• language models are optimized for coherence, not truth
• fluent structure signals authority to human readers
• the model does not internally distinguish “known” from “unknown” unless explicitly constrained

In other words, the model sounds confident because confidence is a linguistic pattern — not a belief state.

Why this keeps getting misinterpreted

Hallucinations are frequently framed as:

• proof of secret memory
• evidence of hidden training data
• signs of emergent awareness

These interpretations confuse:

• output behaviour with internal architecture
• product experience with model capability
• confidence with knowledge

The result is a persistent myth that hallucinations reveal something the system “knows but shouldn’t.”

They don’t.

Why hallucinations are a structural limitation

Unless a model is:

• tightly grounded
• externally verified
• or explicitly constrained to say “I don’t know”

It will always hallucinate in edge cases.

This is not a temporary flaw.
It is a consequence of how generative models work.

Why this matters

Misunderstanding hallucinations leads directly to other false beliefs, including:

• that AI systems secretly store everything users type
• that models remember past conversations across sessions
• that confident answers imply factual access

Those claims build on the same misunderstanding — and collapse under inspection.

Where to go next

• Rumour AI secretly stores everything users type
• Investigation Why AI confidence is mistaken for intelligence