What is a Context Window?

A context window is the maximum amount of text an LLM can process in a single interaction. It includes both your prompt and the model's response combined, measured in tokens.

Think of it as the model's "working memory." Everything outside the context window is invisible to the model, even if it appeared earlier in your conversation.

Why context windows matter

Context window size determines how much information you can provide in prompts and how long responses can be. A model with a 4,000 token window (about 3,000 words) can't generate a complete 2,500-word blog post if your content brief uses 1,000 tokens.

Common sizes vary significantly: GPT-3.5 offers 4,096 tokens (roughly 3,000 words total), GPT-4 provides 8,192 tokens standard or 32,768 tokens extended (6,000-24,000 words), and Claude offers up to 200,000 tokens (150,000+ words). Larger context windows let you provide more detailed prompts, include multiple examples, or generate longer output in one go.

Don't confuse context window with "remembering" previous conversations. Once you exceed the window, earlier content drops out of context entirely. Chat interfaces may store conversation history, but if your conversation exceeds the context window, the model only sees recent messages.

Working within context limits

For long content, generate sections separately rather than trying to create everything at once. Write your H2 sections individually, giving each its own context. Prioritize essential information in prompts - a concise, focused brief often works better than a verbose one that consumes tokens without adding clarity.

When generating AI-generated content, your prompt uses part of the context window, leaving less room for output. If you notice output getting cut off mid-sentence, you've likely hit the limit. Either shorten your prompt, request less output, or break the task into smaller pieces.

Context windows are growing rapidly. Models from 2023 had 4-8K token windows while 2024 models reached 200K+ tokens. Larger windows enable new workflows like providing entire blog post archives as context or including extensive examples without constraint.