An LLM considers a specific amount of text or input while generating a response to a prompt. This is called its context window. Essentially it means the model’s memory. It allows it to analyze certain range of preceding text so as to get a better feel of the context to produce more relevant and coherent responses. A context window of 2000 tokens would allow the model to consider the previous 2000 words of text while generating a response.

The context window size determines a model’s capabilities and performance. A larger window, of course, lets a model generate a more comprehensive answer. However, such large windows also affect computational complexity and memory requirements.

The techniques used by LLMs to manage the context windows are tokenization, positional encoding (position of each token within the context window), attention mechanism (weights are assigned to tokens based on their relevance to the current task), adaptive context windows ( dynamically the conrext window is adjusted to the input and task).

The choice of context window depends on the specific application, the accuracy desired, and the fluency expected. Machine translation, and question answering require large context windows.

GPT-3 has context window of 2048 tokens and GPT-4 has a context window of 32,768 tokens.

GPT-4 Turbo will see more data. It has a context window of 128K. This equals 300 plus pages of that in a single prompt. The larger the context window, the more is the scope for LLM models to understand the question and offer more thought-out and deliberate responses. Previous versions of ChatGPT had context windows of 8K and 32K.

Context window is a fundamental aspect of LLM architecture. It enables the model to process the language and understand it comprehensively and in a context aware manner.

A large context window , apart from higher computational cost, is more likely to generate hallucinations,

As the LLMs evolve, context windows will play a significant role in terms of their capabilities and applications.