How to Write Regular Expressions with AI

A regular expression is a mini-language for describing text patterns. \d matches a digit, + means one or more, [A-Z] is a character class, ^ and $ anchor to the start and end, and parentheses create groups you can capture. Regex works in almost every programming language and in text editors, command-line tools such as grep, and spreadsheet functions. It is also notoriously hard to read, which is exactly why AI helps: you describe the pattern in words and ask for both the regex and a token-by-token explanation. The quality of your description decides the quality of the result. Say what should match and what should not, give three to five positive examples and several negative ones, and name the flavor or engine: JavaScript, Python's re module, PCRE, Java, .NET, POSIX grep, or Google Sheets, which uses RE2. Flavors differ. Lookbehind support, named group syntax and Unicode handling vary, and RE2 deliberately leaves out backreferences and lookaround so that it can guarantee matching in linear time. Then test. Paste the pattern into a tester such as regex101, which highlights matches and explains each part, and run it on edge cases: empty strings, extra whitespace, very long inputs, non-English characters and near-misses. You can ask the AI to generate tricky test strings too, but check them yourself. Common misconceptions include thinking that a regex that matches your examples is correct (it may also match things you never considered), that email or URL validation needs one perfect regex (the full email address standard is very complex, and most teams use a simple sanity check plus a confirmation email), and that regex suits every parsing job. HTML, JSON and nested structures are better handled by a real parser.