Building effective AI applications requires more than just crafting good prompts; providing the right context is crucial. This leads to the question of how much context an AI can truly process, as developers often experience AI assistants forgetting information shared earlier in a conversation. This phenomenon is due to the concept of the context window, a core aspect of modern AI systems. Before understanding context windows, it is essential to grasp the concept of tokens, the basic units of text processed by AI models.Tokens are not equivalent to words; they are smaller pieces of text generated by a tokenizer, which acts as a translator between human language and the AI model. The way text is split into tokens varies, meaning the same number of characters or words can result in different token counts depending on the content's complexity, such as code, JSON, or URLs. Developers must care about tokens because they significantly influence cost, latency, response quality, and scalability.Every AI request consumes tokens, including system instructions, user prompts, conversation history, and retrieved data. When interacting with an AI, the application often sends a large bundle of tokens, even for a simple prompt. Each new AI request typically starts fresh, requiring the application to resend relevant conversation history to maintain coherence. The context window represents the maximum number of tokens an AI model can consider at one time for a single response. Much like a whiteboard, information within the context window is accessible, but the space is finite.Modern AI applications construct a rich context before sending it to the model, often including system instructions, conversation history, retrieved knowledge, and uploaded files, making the user's prompt a small part of the entire input. A larger context window does not automatically guarantee better AI performance; optimizing context by including relevant information and excluding noise is more important. The "Lost in the Middle" problem highlights that information buried deep within a very large context may receive less attention.Crucially, context is not memory; context is the information available for the current request, while memory is data intentionally stored and reintroduced by the application into future conversations. Understanding these distinctions is key to building and interacting with AI effectively.