Intern Builds LLM Chatbot, Uncovers the Hidden Challenge of AI Memory Management

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A developer building a multi-turn chatbot during an internship discovered that LLM APIs are stateless, meaning every request starts fresh with no memory of prior exchanges. This requires developers to manually track and resend conversation history with each API call, a detail rarely covered in basic tutorials. Sending too little history causes the bot to forget context, while sending too much risks hitting token limits and increasing costs. Solving this involved implementing context management strategies, such as summarising older conversation segments rather than discarding them outright. The project also highlighted broader engineering concerns like system prompt design, graceful API error handling, and secure credential management — skills increasingly essential for building real-world AI-powered products.

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