AI chatbots like ChatGPT, Gemini, and Claude often exhibit strange behaviors due to the concept known as the “context window.” This term refers to the short-term memory of AI models that manage conversations using tokens—units of text representing characters or parts of words. Each model has a fixed context window size, which determines how much information it can process at once. When the limit is exceeded, older information may be forgotten, leading to repetitive or vague responses. Matt Pocock explains that this “lost in the middle” problem arises because AI prioritizes the beginning and end of conversations while neglecting the middle. Developers face challenges in coding sessions, as crucial details can be buried within extensive chats. Smaller, well-managed contexts often yield better outputs, enhancing AI performance. Regularly summarizing or resetting conversations can help maintain clarity and prevent context overflow. Thus, understanding and managing the context window is essential for effective AI interactions.
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