Regex Generator

Regular expressions are one of those skills where the syntax is hostile but the payoff is huge. This tool lets you skip the syntax: describe in plain English what you want to match, and it returns a working regex pattern with an explanation and example matches so you can actually verify it does what you meant.

It's built for developers who use regex rarely enough to forget it each time, data analysts cleaning messy text, and anyone who's stared at (?:...) wondering what it does.

How to use it

1. In What should it match?, describe the pattern as precisely as you can: "a US phone number with optional country code and dashes or spaces" beats "a phone number." Edge cases you mention now save debugging later.
2. Pick the Flavor that matches your environment — JavaScript, Python, PCRE, and others differ in lookbehind support, named groups, and escaping. The wrong flavor can produce a pattern that silently fails in your language.
3. Generate, then read the explanation. It breaks the pattern into parts so you understand why it works, not just that it does.
4. Test it against the example matches plus your own real inputs — especially the inputs you expect it to reject.

When to use it

Reach for it when validating form input like emails, URLs, or postal codes; when extracting data from logs or scraped text; or when doing a find-and-replace across a codebase that a plain search can't handle. If you also want to understand an existing pattern someone else wrote, paste it into the Code Explainer for a line-by-line breakdown.

Tips for better results

• Give both positive and negative examples in your description: "match 2026-01-15 but not 15/01/2026." Defining what to reject is half of a correct regex.
• Name the anchoring you need. "The whole string must be a valid email" produces a different (and safer) pattern than "find emails inside text."
• For validation, prefer a pattern that's slightly strict and rejects junk over one that's permissive and lets bad data through.
• Always test against real data, not just the examples — production input is messier than anything you'll imagine.

Common mistakes to avoid

Don't trust a regex you haven't tested against edge cases; the classic failure is an email pattern that accepts a@b or rejects valid addresses with plus-signs. Avoid catastrophic backtracking — patterns with nested quantifiers like (a+)+ can hang on certain inputs, a real denial-of-service risk, so mention performance if the input could be long. And remember that regex is the wrong tool for parsing nested structures like HTML or JSON; for JSON, use the JSON Formatter & Validator instead.

Once your pattern works, lock it in with tests so a future edit doesn't quietly break it. The Unit Test Generator can scaffold cases covering both the strings your regex should match and the ones it should reject, which is exactly where regex bugs hide.