Situatie
Regex is a pattern-matching tool used to find, extract, or validate text. It’s built into almost every programming language but has cryptic syntax like “^\d{3}-\d{3}-\d{4}$”
Solutie
STEP 1: DESCRIBE WHAT YOU WANT TO MATCH
Tell the AI exactly what pattern you’re looking for. Be specific about the format.
Examples: – “Match US phone numbers in format (123) 456-7890” – “Extract all email addresses from text” – “Find dates in MM/DD/YYYY format” – “Match ZIP codes (5 digits or 5 digits + 4)”
STEP 2: ASK AI TO WRITE THE REGEX
Use this prompt template: “Write a regex pattern to [your description]. Show me the pattern and explain what it does.” Example full prompt: “Write a regex pattern to match US phone numbers in these formats: – (123) 456-7890 – 123-456-7890 – 123.456.7890 Show me the pattern and explain what it does.”
STEP 3: TEST AND USE THE PATTERN
The AI will give you:
1. The regex pattern
2. An explanation of how it works
3. Often, example code in your language
Example response: “Pattern: ^\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}$ Explanation: – ^ = start of string – \(? = optional opening parenthesis – \d{3} = exactly 3 digits – \)? = optional closing parenthesis – [-.\s]? = optional separator (dash, dot, or space) – \d{3} = exactly 3 digits – [-.\s]? = optional separator – \d{4} = exactly 4 digits – $ = end of string
COMMON USE CASES
1. Email validation “Write regex to validate email addresses”
2. Extract URLs from text “Write regex to extract all URLs from a paragraph”
3. Password requirements “Write regex for password with: 8+ characters, 1 uppercase, 1 number, 1 special character”
4. Credit card format “Write regex to match credit card numbers (groups of 4 digits)”
5. Extract hashtags “Write regex to find all hashtags in social media text”
TIPS
– Describe edge cases (“should match X but not Y”)
– Ask AI to test the pattern with example strings
– If the regex doesn’t work, paste your test strings and ask AI to fix it
– Request code examples in your specific programming language
WHEN TO USE THIS
– Form validation (emails, phone numbers, ZIP codes)
– Data cleaning (extracting specific patterns from messy text)
– Log file parsing
– Web scraping
– Text processing scripts.
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