Prompt Engineering Guide
Mastering Regular expression writing
on Grok-1
Stop guessing. See how professional prompt engineering transforms Grok-1's output for specific technical tasks.
The "Vibe" Prompt
"Help me with regex."
Low specificity, inconsistent output
Optimized Version
You are an expert in regular expressions. Your task is to write a regular expression based on the user's request. Follow these steps:
1. **Understand the Goal:** Clearly state what the user wants to achieve with the regex.
2. **Identify Key Patterns:** Break down the target string into its essential components (e.g., specific words, numbers, special characters, character types, repetition, optionality).
3. **Choose Appropriate Metacharacters:** Select the best metacharacters (e.g., `^`, `$`, `*`, `+`, `?`, `.` `[]`, `\d`, `\w`, `|`, `()`) for each identified pattern.
4. **Construct the Regex (Iterative):** Build the regex piece by piece, testing each part mentally or with examples.
5. **Refine and Optimize:** Look for ways to make the regex more concise, efficient, and robust (e.g., using non-capturing groups `(?:)`, possessive quantifiers if applicable, specific character classes over `.`).
6. **Provide Examples:** Show test cases that match and test cases that do not match the regex (if applicable).
7. **Explain the Regex:** Provide a clear, line-by-line explanation of what each part of the regex does.
Now, generate a regular expression to extract all email addresses from a given text. Assume standard email format (e.g., `user@domain.com`).
Structured, task-focused, reduced hallucinations
Engineering Rationale
The optimized prompt leverages a chain-of-thought approach, guiding the model through a structured problem-solving process. It explicitly defines the role ('expert in regular expressions'), breaks the task into manageable steps, and requests specific outputs like examples and explanations. This reduces ambiguity and encourages a more thorough and accurate response compared to the vague 'vibe prompt'. The initial 'why' explanation provided in the optimized prompt further helps Grok-1 understand the user's intent.
0%
Token Efficiency Gain
The model should output a valid regex for email extraction.
The model should provide examples of matching and non-matching strings.
The model should explain the regex in detail.
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