Context window

What is a context window?

A context window represents the amount of text an AI language model can process and consider at once. Think of it like the model's working memory—the information it can "see" and reference when generating a response. For large language models like GPT-4, the context window includes both the user's input and the model's previous outputs in a conversation. When you interact with an AI assistant, the context window determines how much of your conversation history and provided information the AI can use to formulate relevant, coherent responses.

How does a context window work in AI models?

AI models process text as sequences of tokens (words, parts of words, or characters). When you provide input, the model converts your text into these tokens and analyzes them within its context window constraints. The model creates mathematical representations (embeddings) of these tokens, capturing their meanings and relationships. As new information enters the window, older information may be pushed out—similar to how recent memories are clearer than distant ones. The model then uses attention mechanisms to weigh the importance of different parts of the available context when generating each new word in its response.

Why is context window size important for AI performance?

Larger context windows significantly enhance AI capabilities by enabling models to handle more complex tasks. With expanded context, models can analyze lengthy documents, maintain coherent discussions over extended conversations, and perform multi-step reasoning. A model with a small context window might forget earlier parts of a conversation, while one with a larger window can maintain continuity and reference information shared much earlier. This expanded memory improves performance on tasks requiring synthesis of widespread information, like summarizing books, analyzing research papers, or understanding complex legal documents.

How are context windows measured?

Context windows are typically measured in tokens rather than words or characters. A token can represent common words, parts of words, or individual characters depending on the model's tokenization method. In English, a token roughly equals about 3/4 of a word on average. For example, "I love artificial intelligence" might be broken into tokens like ["I", "love", "artificial", "intel", "ligence"]. Modern AI models have context windows ranging from a few thousand tokens to over 100,000 tokens. When planning interactions with AI systems, understanding this measurement helps estimate how much information you can provide before exceeding the model's capacity.

What are the limitations of current context windows?

Despite recent advances, context windows still impose significant constraints. Even the largest windows can't accommodate very lengthy documents without summarization or chunking strategies. Models also tend to exhibit "attention dilution," where they struggle to effectively utilize information from the earliest parts of their context window. This creates a recency bias, where the model pays more attention to recent information. Additionally, larger context windows require more computational resources, increasing processing time and costs. These limitations affect applications like document analysis, long-form content generation, and complex reasoning tasks that require maintaining coherence across extensive information.