A dataset is a collection of related data items organized for analysis, processing, or use by a system. In AI development, its usefulness depends on relevance, quality, size, and formatting. Datasets can contain code, question-and-response pairs, structured records, or synthetic examples. section 12.2.1
Use datasets deliberately when preparing data for AI systems:
- Define the intended model behavior before selecting or creating data. section 12.3
- Format records consistently and make the required output explicit. section 6.1.1
- Balance real and synthetic data: synthetic records can supply training material, but purely synthetic data may reduce response quality. section 12.2.1
- Check generated records rather than assuming they satisfy unstated relationships or represent real resources. section 6.1.1
A constrained JSON dataset
A prompt can request five JSON objects and specify each field's name, type, range, and options. A room dataset might include room_name, type, beds, accessible, image, features, and roomPrice. Explicit constraints produce plausible values, but generated image URLs may be placeholders and relationships such as room type versus bed count still require human verification. section 6.1.1
{
"room_name": "Cozy Suite",
"type": "single",
"beds": 1,
"accessible": true,
"image": "https://example.com/room1.jpg",
"description": "Charming room",
"features": ["Wifi", "TV"],
"roomPrice": 150
}
Inspect a concrete JSON record showing mixed data types, arrays, Boolean values, numeric pricing, and a generated URL. section 6.1.1
