Why do neural networks lie and hallucinate?

Neural networks lie for three reasons: training on imperfect data from the internet, limited context (they don't remember what was said 10 messages ago), and a lack of fact-checking — they predict plausible text rather than verifying facts.

Do all neural networks hallucinate?

Yes, all of them. The hallucination rate has dropped from 15% in 2024 to 3-5% in 2026, but no model has reached zero.

How can you check that a neural network isn't lying?

Ask the same question to several different models. If the answers match, it's most likely true. Use Perplexity (it shows sources). Do not use a neural network as your sole source of facts.

Why Neural Networks Lie and Hallucinate

Short answer: Neural networks lie for three reasons: imperfect training data, limited context, and a lack of built-in fact-checking. They don't verify the truth — they predict text that sounds plausible.

Reason #1: Dirty Data

A neural network learns from the internet. And the internet is full of errors, fakes, outdated information, and outright nonsense. When you ask "who invented the radio," the model might output Popov, Marconi, and Tesla — because all three versions were present in the training data. It doesn't know who is right — it knows that people write about all three.

It's like teaching a student from textbooks where half the facts are mixed up. The student will pass the exam, but half the answers will be wrong. It's the same with neural networks.

Reason #2: Limited Context

Every neural network has a "context window" — how much text it can hold in its head at once. In 2024, this was 4-8K tokens (a couple of pages). In 2026 — 128-200K (a whole book). But even 200K tokens is not infinite. If a conversation is long, the neural network "forgets" the beginning of the chat and starts answering inappropriately.

The context problem that existed six months ago is now being actively addressed. Gemini 2.5 holds 1M tokens — that's like "War and Peace" twice over. But fact-checking is still a weak point.

Reason #3: Lack of Fact-Checking

A neural network doesn't Google things. It doesn't verify facts. It predicts the next word. When you ask "how much does a ticket to Mars cost," it doesn't go to the SpaceX website — it "makes up" an answer based on what it has read on the internet. The result: a plausible, but fictional, price.

This is exactly why tools like Perplexity emerged — they combine a neural network with search. The model first searches for sources, then reads them, and only then answers. The hallucination rate drops by a factor of 3-5.

How to Work with This

For facts: Use Perplexity or search-based models. They show sources — you can verify them.

For creativity: Neural networks are ideal. Texts, ideas, creatives — here, hallucinations are a feature, not a bug. Did the neural network "come up with" an interesting headline? Great — that's not an error, that's creativity.

For business: Set up RAG (Retrieval-Augmented Generation) — the neural network will search for answers in your knowledge base instead of "making things up." This reduces the error rate to 1-2%.

I'll set up RAG for your business — the neural network will stop lying about your products. From €500.

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