Just believe everything AI told us... You mean, everything?

AI is useful because it speaks with confidence. That is also exactly why we need to be careful with it.

When an app answers quickly, fluently, and politely, it is easy to feel that it must know what it is talking about. The sentence looks complete. The tone sounds reasonable. The answer arrives faster than any human could check it. Little by little, we start to trust it, then depend on it, and sometimes even argue with real people because the app said something different.

That is the problem of AI hallucination. Doubao's AI output, like the output of any large language model, may sound real without actually being real.

I once saw a small but memorable example at a hospital. The lobby was crowded, chaotic, and uneasy. People were waiting, walking around, asking questions, and trying to figure out where to go. Suddenly, I heard someone questioning a nurse in a harsh voice: “Why doesn't the hospital have this service? Doubao said that you have!”

The nurse looked resigned and replied, “You can't believe everything Doubao said... It tells lies sometimes.”

The person still argued. To me, the scene felt ordinary and strange at the same time. Ordinary, because misunderstandings happen every day. Strange, because the argument was no longer only between a patient and a hospital. There was a third participant in the room: an AI app that had already spoken, sounded certain, and shaped someone's expectation.

This is just a small example of something happening every day in China, where Doubao has nearly become an app used by everybody, from elderly people to kindergarten children. When a tool becomes that common, its mistakes become common too. A hallucination is no longer only a funny screenshot online. It can become a wasted trip, a public argument, a wrong medical expectation, or another moment where people lose trust in institutions because a machine sounded confident.

Some hallucinations are smaller and almost comic. Ask how many “r”s are in “strawberry” or “strawberries,” and some models have answered incorrectly. Humans see the written word and count the letters. A language model may not treat the word as one whole object in the same way. Most of the time, it splits text into chunks, or tokens, and predicts what should come next. That is part of the reason it can stumble over a task that feels embarrassingly simple to us.

Image generation has its own version of the same problem. In the early days, generated people often had hands with four fingers, six fingers, or fingers that melted into each other. The model had learned many patterns of “hand-like” images, but it did not truly understand a hand as a physical structure with five fingers connected by joints. It could produce something visually close while still being anatomically wrong.

So why do these mistakes happen? Briefly, there is no single cause. Sometimes the model leans too hard on patterns it has seen before. Sometimes similar contexts get mixed together. Sometimes the training data is incomplete, noisy, outdated, or too general for a specific local question, such as whether one hospital provides one service. Sometimes the model lacks specific training for a narrow domain. And sometimes the user's question itself contains garbage or ambiguity.

That is the old computing rule: GIGO, Garbage In, Garbage Out. If the input is wrong, vague, biased, or missing key facts, the output can be wrong too. But with AI, there is another layer: even when the input is fine, the model may still generate a plausible answer instead of a true one. It is designed to predict and produce language, not to automatically guarantee reality.

In theory, there may be no way to eliminate AI hallucination completely. Large language models are prediction systems. They estimate likely continuations based on patterns in data. Better training, better retrieval, better tools, and better verification can reduce hallucinations, but they cannot turn probability into certainty in every situation. The same is true for images: better models can improve hands, faces, and details, but they are still generating patterns, not checking the world directly.

This does not mean AI is useless. It means AI should be treated like a powerful assistant, not an unquestionable authority. Use it to draft, brainstorm, summarize, translate, compare, and explain. But when the answer affects money, health, school, travel, law, or another person's work, check it with a reliable source or a real professional.

The real danger is not only that AI sometimes lies. People do too. The danger is that AI can be wrong in a voice that sounds calm, fluent, and official. It can make a guess feel like a fact, and it can make us forget to use our own judgment.

So, should we believe everything AI tells us?