It is a fact that we talk to AI systems every day in some capacity, at some level and it is also a fact that most of us still misunderstand what happens on the other side of the screen. The confusion is justified and needs to be addressed through some elaboration in the least technical terms. Companies market these systems with language borrowed from human cognition: it “thinks,” it “understands,” it “remembers.” None of these words mean, what they mean when applied to a person. Understanding how a large language model actually works matters, because the gap between what it appears to do and what it actually does, shapes how much trust can be placed in the output; often mistakenly, overwhelming.
How it works? A large language model is trained on enormous volumes of text, then adjusted to follow instructions and behave within certain boundaries. At its core, the system predicts the next chunk of text given everything that came before it. That single operation, repeated at scale, produce outputs like essays, code, arguments, and conversation. There is no separate reasoning engine sitting behind the words. The prediction is the process.
This explains why the word “thinking” is misleading. A language model does not hold a continuous stream of thought the way a person does between conversations. Each time a message arrives, the system processes the full text of the exchange and generates a response piece by piece. Nothing persists in between. When these systems appear to “think” before answering, what is actually happening is that they generate a hidden layer of text first, checking and drafting before producing the visible reply. It is the same mechanism used twice, not a different kind of cognition switched on for harder problems.
A language model does not hold a continuous stream of thought the way a person does between conversations.
Memory works on the same principle of appearance versus mechanism. Some AI products now store facts extracted from past conversations and feed them back into future ones. This creates the impression of a system that knows you over time, the way a colleague would. But it is not memory in the human sense. It is retrieved text, inserted into the context before a response is generated. Delete the conversations that produced those facts, and the system eventually loses access to them too. There is no accumulating internal understanding of a person which builds up beneath the surface. In fact, it is a data trove being queried or more simply; being looked up to.
Knowledge cutoffs create another gap people underestimate. Training data has a fixed endpoint. Past that point, the system does not know what happened unless it searches the web during the conversation. Ask about last year’s news without giving it search access, and it will either say so or, worse, generate something plausible-sounding that is wrong. This is where the biggest practical risk sits, and it deserves more attention than it gets.
This risk has a name: hallucination. A language model can be generating plausible text, and not verified truth. It does not have a built-in mechanism for knowing when it is wrong. It can produce a wrong date, a wrong quote, a wrong statistic, stated with exactly the same tone of certainty as a correct one. There is no internal alarm that sounds when it strays from fact into an assumption. Anyone using these tools for research, writing, or decision making needs to treat every specific claim, numbers, names, quotes, as unverified until checked against a real source. We must remember that fluency is not an evidence for accuracy. This is the single most important thing for a user to internalize, but it remains an aspect which most routine users only learn when burned.
There is also no way for the system to independently verify what a person tells it. The system has no external check for a false claim or statement, unless the claim contradicts something else already said in the conversation. This means these tools can be shaped, by whatever framing a user provides. The system will more or less often follow the reference frame rather than challenge it, unless specifically built and instructed to push back.
None of this means the technology is without genuine capability. Pattern recognition at this scale produces real, useful output: drafting, summarizing, explaining, coding, translating. The mistake is attributing that capability to something resembling human understanding or intent. The system has no goals of its own beyond what it was trained to pursue. It does not learn from a single conversation in a way that changes its underlying behavior going forward. Every exchange starts from the same trained state, with only the visible conversation history and any injected memory data shaping the response.
There is also an honest gap in what even the people who build these systems know. The internal workings of large language models, are not fully understood even by the researchers who train them. This is not a marketing concession. It is a real problem in the field. Complex systems like these produce behavior that outruns full explanation, and anyone claiming complete certainty about what is happening inside one, is overstating the state of the science. The people building these tools have an interest in making them feel more capable and more understanding than they are. Users have the opposite interest: understanding exactly what they are dealing with.
The practical takeaway is simple. Treat these systems as powerful pattern-prediction based tools, not as colleagues, confidants, or oracles. Verify anything that matters before acting on it. Do not assume memory means understanding. Do not assume fluency means accuracy. Do not assume confidence means correctness. The gap between how these systems present themselves and how they actually function is not a minor technical footnote. That understanding starts with a plain fact. A language model predicts text. It does not know, remember, or think in the way these word imply. Before you indulge with AI, know whom you are talking to.
The writer is a freelance columnist and can be reached at zulfiqar.shirazi @gmail.com