How to build a profanity filter that actually works

TL;DR: A production-ready profanity filter isn't just a list of banned words; it's a pipeline. You start with sanitization to normalize character substitutions, followed by a Trie for efficient prefix matching. To avoid the Scunthorpe problem, you cross-reference matches against an allow-list or use context-aware ML models to score intent, balancing raw speed with semantic accuracy. Building a content filter seems like a Junior-level task until you actually have to deploy it to a live chat or a comment section. If you just use a regex or String.contains() on a list of banned words, you’ll quickly realize that users are incredibly creative at bypassing filters. Whether it's adding a period ( b.u.m ), using leetspeak ( b@m ), or hiding a word inside a valid one ( bumpy ), a simple search-and-replace won't cut it. You need a multi-stage pipeline that balances performance with accuracy. How do you handle character substitutions and leetspeak? Sanitization normalizes the input before it eve