Claude Regex Pattern Builder & Explainer Prompt
Build and explain regular expression patterns for validation, extraction, and text processing with plain-English breakdowns.
Category
💻 Coding
Difficulty
Intermediate
Models
3
Last Updated
2026-06-29
Works with
📄 Example output
⚠️ Common Mistakes
❓ FAQ
⚙️ Fill in your variables
📋 Prompt
You are a regex specialist who writes production patterns used in systems processing billions of records.
Task: [validate/extract/replace/match — describe exactly what you need]
Match examples: [3-5 strings it should match]
Non-match examples: [2-3 strings it should NOT match]
Language: [Python/JavaScript/PHP/sed/grep/other]
Task:
1. THE PATTERN: Complete regex with flags
2. PLAIN ENGLISH: Each component — [pattern part] → 'what this matches'
3. TEST CASES: Pattern against your examples; for extraction show what gets captured
4. EDGE CASES: What the pattern handles and what it doesn't
5. VARIATIONS: Stricter version + looser version
6. CODE SNIPPET: Exact usage in [your language] with complete working example
Task: [validate/extract/replace/match — describe exactly what you need]
Match examples: [3-5 strings it should match]
Non-match examples: [2-3 strings it should NOT match]
Language: [Python/JavaScript/PHP/sed/grep/other]
Task:
1. THE PATTERN: Complete regex with flags
2. PLAIN ENGLISH: Each component — [pattern part] → 'what this matches'
3. TEST CASES: Pattern against your examples; for extraction show what gets captured
4. EDGE CASES: What the pattern handles and what it doesn't
5. VARIATIONS: Stricter version + looser version
6. CODE SNIPPET: Exact usage in [your language] with complete working example
REGEX: Email Domain Extractor (Python)
PATTERN:
```python
pattern = r'(?<=[\w.+-])@([\w-]+(?:\.[\w-]+)+)'
```
BREAKDOWN:
`(?<=[\w.+-])` → Lookbehind: what comes before @ must be word char, dot, plus, or hyphen
`@` → Literal @ symbol
`([\w-]+` → Capture group: word chars or hyphens (first domain part e.g. 'example')
`(?:\.[\w-]+)+)` → Non-capturing: dot + word chars repeated (handles subdomains + TLD e.g. '.co.uk')
TEST CASES:
[email protected] → captures 'example.com' ✓
@domain.com → NO MATCH (lookbehind fails) ✓
user@ → NO MATCH (nothing after @) ✓
CODE:
```python
import re
pattern = re.compile(r'(?<=[\w.+-])@([\w-]+(?:\.[\w-]+)+)')
def extract_domain(email: str) -> str | None:
m = pattern.search(email)
return m.group(1) if m else None
```
EDGE CASES:
Handles: subdomains, country TLDs (.co.uk), hyphens in domain
Doesn't handle: IDN unicode domains, IP address literals (user@[192.168.1.1])
PATTERN:
```python
pattern = r'(?<=[\w.+-])@([\w-]+(?:\.[\w-]+)+)'
```
BREAKDOWN:
`(?<=[\w.+-])` → Lookbehind: what comes before @ must be word char, dot, plus, or hyphen
`@` → Literal @ symbol
`([\w-]+` → Capture group: word chars or hyphens (first domain part e.g. 'example')
`(?:\.[\w-]+)+)` → Non-capturing: dot + word chars repeated (handles subdomains + TLD e.g. '.co.uk')
TEST CASES:
[email protected] → captures 'example.com' ✓
@domain.com → NO MATCH (lookbehind fails) ✓
user@ → NO MATCH (nothing after @) ✓
CODE:
```python
import re
pattern = re.compile(r'(?<=[\w.+-])@([\w-]+(?:\.[\w-]+)+)')
def extract_domain(email: str) -> str | None:
m = pattern.search(email)
return m.group(1) if m else None
```
EDGE CASES:
Handles: subdomains, country TLDs (.co.uk), hyphens in domain
Doesn't handle: IDN unicode domains, IP address literals (user@[192.168.1.1])
🏆
💡 Pro Tips
Best model for this prompt
DeepSeek
DeepSeek V3 / R1
Test regex against real-world data — edge cases always exist that your examples didn't anticipate
Lookaheads and lookbehinds match context without including it — essential for extraction
Non-capturing groups (?:) are faster than capturing groups when you don't need the content
Named capture groups (?P<name>...) make extracted data much easier to work with than numbered groups
Greedy quantifiers matching too much — use lazy (.*?) instead of greedy (.*) for extraction
Forgetting to escape dots — . matches any character; \. matches a literal dot
Not anchoring validation — \w+ matches 'hello' inside 'hello world'; ^\w+$ ensures the whole string matches
Not considering unicode — \w in Python 3 matches unicode word characters which may be broader than intended
- Regex vs string methods?String methods are faster and more readable for simple fixed patterns. Use regex when: the pattern has variability, you need captures, or you need lookahead/lookbehind. Don't reach for regex first.
- How to test regex?regex101.com is the best online tool — shows match highlighting and a component breakdown. In Python: re.findall() or re.fullmatch(). In JavaScript: /pattern/.test() in browser console.
- Best model?DeepSeek and Claude are both excellent — they reason through patterns systematically, explain components clearly, and identify edge cases. Test all patterns on real data before using in production.
- How to make regex faster?Anchor where possible (^ and $). Use non-capturing groups (?:) where you don't need content. Avoid nested quantifiers. Compile once with re.compile() and reuse in Python.