Why artificial intelligence does not learn like a human

Many people think that an AI model learns directly during a conversation: you correct it, it remembers, and becomes smarter. In reality, this is not how most chatbots work. The model does not change during a dialogue, and what looks like memory works in a completely different way. Below, we’ll look at how an AI model actually learns, where its “memory” comes from, and how all of this differs from human learning.

How an AI model learns: two stages

Pretraining

A language model such as GPT or Claude processes a vast amount of text and learns to predict the next word—or, more precisely, the next fragment of a word, known as a token. An enormous number of these attempts adjusts the model’s internal parameters, its weights. The weights store everything the model “knows”: grammar, facts, styles, and reasoning techniques.

This stage takes weeks or months on thousands of specialized accelerators and is extremely expensive. That is why it is performed infrequently and only by developers.

Fine-Tuning

After pretraining, the model is fine-tuned: it is shown examples of good answers, human evaluators are asked to compare response variants, and safety rules are added. This turns the model from a “text completer” into an assistant that answers questions. Developers also handle this stage, and the result is released as a new model version.

What happens when you “correct” an AI

Within a single dialogue, the model does take your words into account. But it does not need to learn in order to do this: everything you wrote earlier is added to the prompt as part of the text. The amount of this text is limited by the context window. As long as your correction fits within the window, the model relies on it. If the dialogue becomes too long, the beginning “falls out,” and the model may return to its previous mistake. If you open a new chat, everything starts from scratch.

This means that within one chat, the model can adapt to your style or response format, but this is not knowledge—it is a prompt-based hint. You can reproduce it simply by inserting the same text into another chat.

What about “memory” in ChatGPT and Claude?

Some services, including ChatGPT and Claude, offer a memory feature: the service saves short notes about you—your name, preferences, and work context—and adds them to the beginning of future dialogues. Essentially, this is a notebook next to the model, not a modification of the model itself. You can usually view, edit, or delete these notes in the settings.

Document search tools, such as RAGRAG Retrieval-augmented generation: the model answers from passages found in your documents. CustomGPT: a no-code RAG chatbot, work in a similar way. The answer is based on relevant excerpts from your files, which are added to the prompt rather than becoming new knowledge for the model. Examples of such services include NotebookLM and Perplexity.

Are your conversations used for training?

Another question is whether your conversations are used to train future versions. This depends on the service, plan, and privacy settings: some services allow data usage to be disabled, while it is generally not used for corporate plans. If you work with confidential information, check the settings and terms of the specific service you use. However, even when data is used, it is incorporated into the training of a new version later—it does not change the model during the conversation.

How this differs from humans

The last point is debatable: scientists are still discussing whether what a model does can be considered “understanding.” But the practical conclusion remains the same: a model works reliably with things that resemble its training data and makes mistakes in unfamiliar situations.

Why you cannot simply train a model on the fly

Researchers are working on continuous learning, but it has a serious problem: catastrophic forgetting. When a model is further trained on new data, it may lose previously acquired skills. In addition, if a model learned from any conversation, it could easily be “taught” something incorrect, allowing one malicious user to change its behavior for everyone. That is why developers update models through releases after testing them.

Practical takeaways

• Verify facts. The model’s knowledge is limited by its training cutoff date, while fabricated details can sound convincing. For up-to-date information, use a mode with search or a service that provides sources.
• Provide the context yourself. If you need a specific style or format, include it in your prompt or save a template. Do not expect the model to remember it on its own.
• Start a new chat for a new topic. A long dialogue covering different topics can confuse the model.
• Do not enter confidential data unless you are certain about the privacy settings.
• Do not expect “growth” through interaction. The model will not become smarter simply because you talk to it, but it will become more useful if you formulate tasks more clearly. Read more about this in our guide to types of neural networks.

In short

An AI model learns once, through its developers, while during a conversation it uses what is already stored in its weights, along with the text of your prompt and, if enabled, saved memory notes. This is not a drawback but a feature of how it is designed: understanding its limitations helps you use AI responsibly and assign it tasks it can handle. You can choose a service for your specific needs in the AI tools catalog.

Source of inspiration and more IT content: news.kh.ua/it