Structured Data Extraction
Extracting structured data from unstructured text is one of the most valuable AI applications. Transform messy input into clean, usable data.
What is Data Extraction?
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Extraction Strategies
1. Define Fields First
Tell the AI exactly what to look for:
Extract these fields:
- Name
- Email
- Phone
- Company
2. Provide Schema
Give structure for the output:
{
"name": string,
"email": string or null,
"phone": string or null
}
3. Handle Missing Data
Specify behavior for absent information:
Use null for missing fields
Use "Unknown" for missing strings
Use -1 for missing numbers
Exercise: Extract Multiple Entities
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Extraction Patterns
Simple Key-Value
Extract: Name, Email, Phone
Format: {"name": "", "email": "", "phone": ""}
Lists and Arrays
Extract: All product names mentioned
Format: {"products": ["item1", "item2"]}
Nested Objects
Extract: Company with multiple contacts
Format: {"company": "", "contacts": [{"name": "", "role": ""}]}
Relationships
Extract: Mentions of Person A reporting to Person B
Format: {"relationships": [{"subordinate": "", "manager": ""}]}
Handling Ambiguity
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Exercise: Invoice Data Extraction
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Normalization
Transform extracted data into consistent formats:
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Quality Indicators
Add confidence or source tracking:
{
"extracted": {
"revenue": "$5M",
"confidence": "high",
"source": "explicitly stated"
},
"inferred": {
"growth_rate": "20%",
"confidence": "medium",
"source": "calculated from context"
}
}
Practice: Resume Extraction
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Structured data extraction turns AI into a powerful parsing tool for any text format.
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