In this comprehensive guide, we'll explore how artificial intelligence and JSON work together to solve a common data standardization challenge: converting unstructured data into a consistent, machine-readable format.

Through a practical example of standardizing company addresses from various sources, we'll demonstrate the power of combining AI's natural language processing capabilities with JSON's structured data format.

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1. Introduction to JSON

• Definition: JSON (JavaScript Object Notation) is a standard format for sending data between systems, typically via an API.
• Importance: Useful for transforming strings of text into structured JSON outputs.

2. Specific Use Case

• Objective: Extract and standardize company addresses into a specific format.
• Required Format: Street address, city, state, zip code, and country code.

3. Data Collection Process

• Initial Step: Check LinkedIn for the company address.
• Secondary Step: If the LinkedIn address is unavailable, utilize Clagint to retrieve the address.
• Result: Obtain a company address string containing various components (e.g., street name, apartment number, city, etc.).

4. Address Standardization Challenges

• Variability in Data: Countries may be spelled out or abbreviated (e.g., "United States" vs. "US").
• Case Sensitivity: Different parts of the address may have inconsistent capitalization (e.g., "California" vs. "california").

5. JSON Implementation

• Purpose: To transform the unstructured address string into individual, standardized columns.
• Method: Use a GPT prompt to parse the address into structured JSON format.

6. Creating the GPT Prompt