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Prompt Engineering
- Introduction
- Model Basics
- Prompt Structures
- Clarity & Specificity
- Using Context
- Role Instructions
- Step-by-Step
- Handling Ambiguity
- Creativity vs Precision
- Using Examples
- Advanced Techniques
- Troubleshooting
- Common Pitfalls
- Evaluating Quality
- Real-World Examples
- Prompt Templates
- AI Tasks
- Safety & Ethics
- Multimodal Prompts
- Data Extraction
- Conversation
- Personalization
Data Extraction
You can use prompts to extract structured data from text. Data extraction is useful for tasks like pulling out names, dates, email addresses, or other key information from unstructured content. Well-structured prompts help the AI understand exactly what data you need and how it should be formatted.
Why Use Data Extraction Prompts?
- Automates tedious tasks: Quickly extract information without manual review.
- Improves accuracy: Clear instructions reduce errors and omissions.
- Enables downstream processing: Structured outputs can be used in databases, spreadsheets, or other tools.
Example
Extract all email addresses from the following text and list them:
{text}Expanded Example:
Extract all dates, email addresses, and phone numbers from the following customer feedback. Present the results in a table with columns for 'Type' and 'Value.'
{text}Be specific about the format you want for extracted data. If you need a table, list, or JSON output, mention it in your prompt.
Best Practices for Data Extraction
- Specify the data types you want to extract (e.g., names, dates, locations).
- Request a specific output format (e.g., table, list, JSON).
- Test your prompt with different types of input to ensure reliability.