Data model documentation using LLM templates

When creating data model documentation using Large Language Models (LLM) templates, the goal is to automate and enhance the process of documenting complex data structures. This can help maintain consistent, readable, and accurate documentation for data models that can evolve with the data and business logic.

Here’s a template to help document a data model:

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1. Introduction to the Data Model

• Overview:
  Provide a high-level summary of the data model, including its purpose, scope, and any key objectives it aims to achieve. This section should describe why the model exists and how it fits within the overall system architecture.

  Example:
  “This data model represents the eCommerce platform’s inventory management system, focusing on product catalog, stock levels, and order tracking.”

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2. Data Model Structure

• Entities and Relationships:
  Describe the key entities in the model, their relationships, and how they interact. This section should include an overview of the tables (or objects) and how they link to each other.

  Example:
  “The system includes entities such as Product, Category, Order, and Customer. A Product belongs to a Category and can be part of many Orders. Each Order is placed by a Customer.”

• Entity Descriptions:
  List all entities and describe them in detail. For each entity, provide the following:

  • Entity Name:
    Name of the entity (e.g., Product, Order)

  • Attributes:
    List of attributes within the entity (e.g., Product ID, Product Name, Price)

  • Primary Key:
    Identify the unique identifier of the entity.

  • Foreign Keys:
    List relationships to other entities via foreign keys.

  • Description:
    A detailed description of what the entity represents.

  Example (for Product entity):

  • Product:

    • Attributes: ProductID, ProductName, Price, StockQuantity, CategoryID

    • Primary Key: ProductID

    • Foreign Keys: CategoryID (References Category)

    • Description: “Represents a product in the catalog, with relevant details such as name, price, and stock level.”

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3. Data Types and Constraints

• Attribute Types:
  Specify the data types for each attribute (e.g., VARCHAR, INT, DATE, DECIMAL).

  Example:

  • ProductName (VARCHAR(255)): The name of the product.

  • Price (DECIMAL(10, 2)): The price of the product, in decimal format.

• Constraints:
  Detail any constraints applied to the attributes or entities (e.g., NOT NULL, UNIQUE, CHECK, etc.).

  Example:

  • Price: CHECK(Price >= 0) — Ensures that the price cannot be negative.

  • StockQuantity: NOT NULL — Ensures that stock levels are always provided.

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4. Data Flow and Relationships

• Relationship Mapping:
  Describe how entities are related to one another using relationships like one-to-one, one-to-many, or many-to-many. Visual diagrams or ER (Entity-Relationship) diagrams can be added here for clarity.

  Example:
  “The Product entity has a one-to-many relationship with the Order entity, where each product can be part of multiple orders, but each order contains one specific product at a time.”

• Example of Data Flow:
  Show how data moves between entities in real-world use cases. For instance, when a customer places an order, an Order entity is created and associated with a Customer, while each Product included in the order is updated with new stock quantities.

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5. Indexes and Optimization

• Indexes:
  Identify any indexes on tables to improve query performance. Include information on the columns indexed and the reasons behind the indexing choice.

  Example:

  • ProductName has an index for fast search operations.

  • Composite index on OrderDate and CustomerID for optimizing order history queries.

• Optimization Techniques:
  Provide insight into any optimization strategies applied, such as denormalization, partitioning, or sharding, and how they improve performance.

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6. Security and Access Control

• Access Levels:
  Describe any user roles or permissions related to the data model. Who can read, write, or update specific data?

  Example:
  “Admin users have full access to all entities, while Customer Service users only have read access to the Order and Customer entities.”

• Sensitive Data:
  Identify if any attributes contain sensitive data and outline encryption or protection measures in place.

  Example:
  “The Password attribute in the Customer entity is encrypted to ensure data security.”

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7. Data Validation and Business Rules

• Validation Rules:
  Describe any rules enforced on the data during input or processing. For example, if the data model requires certain attributes to adhere to specific formats or ranges.

  Example:
  “The Price attribute must always be a positive number greater than zero.”

• Business Rules:
  Provide any business logic associated with the data model, such as conditions or calculations that depend on the data.

  Example:
  “If an order exceeds a total of $500, apply a 10% discount.”

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8. Version History

• Versioning:
  Keep track of changes made to the data model over time, including updates, new entities, or removed entities. Document each version with its associated date and changes.

  Example:

  • Version 1.0 — Initial creation.

  • Version 1.1 — Added Discount attribute to Product.

  • Version 2.0 — Introduced OrderStatus enumeration.

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9. Example Data

• Sample Data:
  Provide example records for each entity to give stakeholders an understanding of how the data model is used in practice.

  Example:

  • Product:

    • ProductID: 1

    • ProductName: “Laptop”

    • Price: 999.99

    • StockQuantity: 50

    • CategoryID: 3

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This template outlines a comprehensive approach to documenting a data model with the help of LLM templates. The document can be customized to suit the particularities of your specific data model, including its business rules, relationships, and the overall architecture of the system.