When AI gives a wrong answer, I don't usually think that it forgets something. My first reaction is that the model is just not that smart. And only later I might think something like this, wait, didn't I already provide this information, told it before? So when a model doesn't know something, we tend to blame either intelligence or memory. But if we narrow down the problem to one specific type of failure, missing knowledge, then there are only a couple of structured possibilities. Let's separate them clearly. The first one, the model was never trained on that information.
If something was never part of the training data set, it simply cannot retrieve it. It doesn't know about it. Think of confidential company documentation, private emails, an internal database. It doesn't matter how obvious this information seems to you. If it wasn't in the training data set, the model never knew it. And that's the training limitation. The second one, the information exists in the real world, but the model provider didn't inject it into the current system. This often happens with events like some news, something happened yesterday, and it happened normally after the cutoff date.
So you might think that the model should for sure know about this information, because I know it and the model is normally smarter than I am. But unless that information is added into the model's accessible context window through updates, browsing, retrieval, or other mechanisms, the base model still does not have it. From the model's perspective, it never received that knowledge. So again, it doesn't know. And the third one, the model could know it, and maybe you even have provided it yourself in the chat, but it cannot see it right now.
Language models operate only on what is inside the current context window. If you shared something many messages ago and the conversation became long, earlier parts may fall outside the visible context window. When that happens, it looks like forgetting. But it's not really a memory loss. It's the visibility limit. So practically, there are three situations. First one, it was never in the training dataset. Another one, it exists, but it has never been injected into the system. And the third one, it was provided earlier, but it's no longer inside the context window.
In all cases, the model is not being stupid. It is operating strictly within structural limits. Even though you cannot directly see the existing boundaries or exact boundaries of the context window, you still can control it. If something is critical, you can just restate it. If something is recent, provide it again. If something is private, include it explicitly in the chat. Because the model can only work with what it was trained on and what it can currently see inside the context window. Before blaming intelligence or memory, ask a simpler question.
Where would this knowledge have come from? Training, from provider update, or from this conversation? First, you should understand this and then fix the right layer. This was a note on AI. Thank you for watching, and I'll see you in the next one. Bye.
