Designing Data Structures With AI
Good data structures make code simpler. When your data is organized well, the code that works with it becomes clearer and easier to write. AI can help you design structures that fit your project's needs.
What Is Data Modeling?
Data modeling means deciding how to represent information in your program. You ask questions like:
- What entities exist in my system? (Users, products, orders)
- What properties does each entity have? (Name, price, date)
- How do entities relate to each other? (A user has many orders)
The answers shape your data structures — the dictionaries, lists, objects, or database tables that hold your information.
Thinking Through Your Data
Before asking AI for help, think through what data you actually have:
What information do you need to store? List everything, even if it seems obvious.
How will you use this data? Will you search it? Sort it? Display it? Update it?
What operations are most common? Looking up by ID? Filtering by date? Calculating totals?
These questions guide structure decisions. Data you search frequently might need different organization than data you only display.
Using AI to Design Structures
AI excels at suggesting data structures. Describe your needs and ask for recommendations:
"I'm building a stock portfolio tracker. I need to store:
- Stock symbols and names
- Purchase date and price
- Current price
- Quantity owned
What data structure would you recommend?
Show me an example in Python and JSON."
The AI might suggest a list of dictionaries, a class-based approach, or a different structure entirely — and explain why each option has tradeoffs.
Example: Portfolio Data
Here's how AI might structure portfolio data:
As Python dictionaries:
portfolio = [
{
"symbol": "AAPL",
"name": "Apple Inc.",
"quantity": 10,
"purchase_price": 150.00,
"purchase_date": "2024-01-15",
"current_price": 175.50
},
{
"symbol": "GOOGL",
"name": "Alphabet Inc.",
"quantity": 5,
"purchase_price": 140.00,
"purchase_date": "2024-02-20",
"current_price": 155.25
}
]
As JSON:
{
"holdings": [
{
"symbol": "AAPL",
"name": "Apple Inc.",
"quantity": 10,
"purchase_price": 150.00,
"purchase_date": "2024-01-15"
}
]
}
Tradeoffs in Structure Choices
Different structures have different strengths:
- Lists are good for ordered collections you iterate through
- Dictionaries are good for quick lookups by key
- Nested structures represent relationships but add complexity
Ask AI about tradeoffs: "What are the pros and cons of using a dictionary vs a list for this data?"
Understanding tradeoffs helps you make informed choices rather than guessing.