Context windows and token limits for AI product managers

Understanding context windows is critical for AI features that remember inputs. If your product loses early details, you are likely hitting context window token limits. This lesson uses a packing analogy to explain how language models process data. Interactions share a single token budget, including instructions, chat history, documents, the prompt, and the reply. When full, chatbots quietly drop the oldest messages. We explore why upgrading to a larger context window is not a free fix. Bigger windows increase costs and slow responses. You will also learn about the lost in the middle effect, where models ignore buried details. Through a support bot example, we show how AI forgets an order number. You will learn mitigation strategies like summarizing old history and placing critical instructions at the ends. In this lesson: - How context windows limit AI memory - Why chatbots forget early messages - Cost and speed tradeoffs of larger windows - Strategies to manage token budgets U2xAI Academy - AI skills for product managers.