Image credit: bdtechtalks

In every interaction with an AI model, there’s a limit to how much information it can process at once. This is known as the context window, and it includes both the prompt you provide and the response the model generates. The model doesn’t just read the input and reply, it analyzes token by token and referencing patterns from it’s training data. Each token is weighted for relevance and likelihood, and the model calculates the most probable next token based on the available content within that window.

Imagine you are reading a book, but you can only remember the last few pages. If someone asks about you a part from earlier in the story, you might take a guess or sometimes get it wrong. That’s how AI works too. Every model has a working memory span, and when conversations exceed that limit, earlier parts may be forgotten. This is when hallucinations happen, responses that sound plausible but are disconnected from the original input.

That’s why understanding the context window is important when designing AI interactions. Long prompts or ongoing chats should be split into a series of focused steps to stay within the model’s memory limit. This not only improves the accuracy and clarity of response but also prevents loss of key details. By tailoring the prompts to fit within the context window, we can enhance the quality and relevance of the model’s responses.