Regex Generator — Turn Plain English Into a Regular Expression

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Regex Generator

Describe what you need in plain English and instantly get a verified regular expression.

Examples:
Generated Pattern
/^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$/gm

Matches user mailbox name, @ symbol, valid domain name, and at least a 2-letter top-level domain.

Matching example
developer@example.com
Non-matching example
developer@com

Generated regular expressions should always be tested thoroughly against your real production data and edge cases.

Your regex and test data stay in your browser.
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Regex Generator — Turn Plain English Into a Regular Expression

Describe the shape of the text — an email, a version tag, a log line, a slug — and get back a pattern you can read, adjust and paste. Every generated pattern comes with the token-by-token explanation so you can extend it instead of trusting it.

  • Start from a template, then edit the generated pattern in the tester.
  • Ask for the strictest pattern that your real data satisfies; loose patterns pass tests and fail production.
  • Add one failing example for every pattern you ship, alongside one passing one.

This tool runs entirely in your browser. Your patterns and text are not uploaded, stored, or logged.

Generate, then narrow

A generated pattern is a starting point, never the final answer. The generator produces the conventional pattern for a shape — anchors, character classes, quantifier bounds — and your job is to tighten it against data you actually have. Ask it for an email pattern, then decide whether you really want to accept quoted local parts, plus-addressing, or internationalized domains.

The fastest loop is generate, paste into the tester, paste twenty real values from your logs, and count the false positives. Patterns that were never run against real input are how validation ends up accepting nothing or everything.

Validation patterns vs extraction patterns

These two need different shapes even for the same data. Validation wraps the pattern in `^` and `$` and answers yes or no; extraction searches inside a larger body of text and captures the parts you keep. A pattern written for one silently misbehaves in the other — an unanchored validator matches a substring, an anchored extractor matches nothing.

  • Validation: anchor both ends, no capture groups needed.
  • Extraction: keep it unanchored, capture only what you use, and prefer named groups for readability.
  • Sanitizing: match what you want to remove precisely, or remove nothing at all.

Why generated regex still needs tests

Regex bugs are usually boundary bugs: an empty string, a value at the exact length limit, an unexpected separator, a string that contains the pattern plus more. A one-line test in your suite costs seconds and catches every future edit to the pattern.

For anything that runs on untrusted input, also check the pattern for nested quantifiers such as `(a+)+`. That is the shape that turns into catastrophic backtracking, and the tester surfaces its guard when a pattern starts to misbehave.

Frequently asked questions

Can I generate a regex from a description without writing syntax?

Yes. Pick or describe the shape of the text you need to match and the tool returns a JavaScript pattern with recommended flags, plus the explanation of each token so you can adjust it rather than guess.

Does the generator support every regex feature?

It targets the ECMAScript dialect used by Node.js and browsers, including named groups and lookarounds. Features outside that dialect, such as PCRE possessive quantifiers, are deliberately not produced because the runtime would reject them.

Should I use a generated pattern straight in production?

Only after testing it against your own data. Generated patterns are conventional and readable, but length limits, allowed separators and anchoring are product decisions that only your input corpus can settle.

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