Video narration
How can your spam filter improve instead of following only fixed instructions? When a new message arrives, its data-words, links, and sender details-enters a model, a tool that estimates: spam or not spam. That decision is the output, but the model is not the whole system. The architecture-the way the parts are connected-moves incoming data to the model, sends the decision onward, and brings results back as feedback. If you mark a message as spam, or correct a mistaken decision, that feedback becomes a new example. It helps the model respond when real spam changes. So the flip is simple: an intelligent system is not merely a model producing an answer. It is a connected loop that uses feedback to adapt. Build the simple spam-filter model this way: feed email data in, let the model decide, and use corrected results to improve when new spam appears.