#Crisis Detection False Alarms
The chatbot initially had trouble distinguishing between casual expressions and genuine crisis situations. For example, it would flag phrases like "I'm dying to try that cake" as concerning. I solved this by implementing context-aware detection that looks at surrounding words and phrases to better understand the user's intent. This dramatically reduced false positives while maintaining sensitivity to genuine crisis situations.
#Message Ordering Issues
A tricky bug emerged in the chat system where messages would sometimes appear in the wrong order. This was especially problematic during rapid conversations or when multiple users were chatting simultaneously. The solution involved implementing proper timestamp handling and message queuing. Each message now gets a precise timestamp, and the system ensures messages are always displayed in the correct chronological order.
#Memory Problems with the Chatbot
The AI chatbot started experiencing performance issues after long conversations because it was storing the entire chat history indefinitely. This led to slower responses and increased server load. I fixed this by implementing a "sliding window" approach where the bot only keeps track of the most recent messages while maintaining enough context to have meaningful conversations.
#Browser Security Restrictions
When testing the integration between the frontend and backend, the application kept running into security restrictions from web browsers. This prevented the frontend from properly communicating with the backend server. I resolved this by properly configuring security settings and implementing the correct protocols for cross-origin resource sharing, ensuring secure but functional communication between different parts of the application