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On this page:
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🧠 Product Docs
🤖 Prompting Tips
💻 Use Cases
📚 Resources
🍄 About Expanse
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Every message you send to an LLM provides the AI with the information and ‘context’ to generate a relevant response. Each time you send a message, your entire chat history gets sent. Therefore, the longer your conversation, the greater the context. While having more context can provide the AI more information to draw good results, this can also confuse the AI as it has too much information to process before responding to your prompt. ** Context management helps keep the information the AI processes relevant and concise and managing your context well will get you better results while saving you tokens/money.
The amount of context or information an AI can hold, relies on the size of the Context Window. You can think of an LLM’s Context Window as the equivalent of its working memory. It determines how well it can carry out tasks without forgetting details from earlier in the conversation. It also determines the maximum size of documents or code samples that it can process at once. Therefore, the larger the context window, the more information the AI is able to hold, review and respond to.
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Context window size is measured in Tokens*.* A common approximation is that one token corresponds to about 0.75 words.
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Get a feel for context size:
| Tokens | Approximate Text Length | LLMs with Comparable Context Windows |
|---|---|---|
| 256 | A short tweet or text message | • |
| 1,000 | A dense email or news article | • |
| 4,000 | ~3 pages (short story, transcript) | GPT-3 |
| 8,000 | ~6 pages (longer article, essay) | GPT-3.5 |
| 32,000 | ~24 pages (novella, detailed report) | GPT-4 |
| 100,000 | ~375 pages (small book, screenplay) | Claude 3.5 Sonnet |
| 200,000 | ~750 pages (long novel, technical manual) | Claude 3.7 Sonnet |
| 1,000,000 | ~3,750 pages (large encyclopedia, 500 images) | GPT-4o (OpenAI's upcoming model with extended context window) |
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Expanse gives you some great tools to improve your context management: