About Context Window in AI:
- The Context Window of an Artificial Intelligence (AI) model measures how much information the AI model can remember, working similarly to humans’ short-term memory.
- AI models don’t read words; instead, they read chunks of characters called tokens.
- Context Window is the amount of text, in tokens, that the model can consider or “remember” at any one time.
- A larger context window enables an AI model to process longer inputs and incorporate a greater amount of information into each output.
- A Large Language Model’s (LLM’s) context window can be thought of as the equivalent of its working memory.
- It determines how long of a conversation it can carry out without forgetting details from earlier in the exchange.
- It also determines the maximum size of documents or code samples that it can process at once.
- When a prompt, conversation, document, or code base exceeds an AI model’s context window, it must be truncated or summarized for the model to proceed.
- Generally speaking, increasing an LLM’s context window size translates to increased accuracy, fewer hallucinations, more coherent model responses, longer conversations and an improved ability to analyze longer sequences of data.
- However, increasing context length is not without tradeoffs: it often entails increased computational power requirements—and therefore increased costs—and a potential increase in vulnerability to adversarial attacks.
