Comparison
Sampling vs Structured Output
Sampling and Structured Output are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Sampling
Sampling comes up when the question is fundamentally about inference.
OpenAI default: temperature 1.0, top-p 1.0.
When you would reach for Structured Output
Any time you need to programmatically parse model output — extraction, function arguments, classification, multi-step pipelines.
OpenAI Structured Outputs with a Pydantic / JSON Schema.
Frequently asked
What is the difference between Sampling and Structured Output?
Sampling: Sampling is the act of choosing the next token from the model's output distribution, typically after applying temperature and a truncation strategy like top-p or top-k. Structured Output: Structured output constrains an LLM to emit text matching a schema — usually JSON. The model can be guaranteed to produce valid output that your code can parse without retries.
When should I use Sampling vs Structured Output?
Sampling is the right concept when you are focused on inference. Any time you need to programmatically parse model output — extraction, function arguments, classification, multi-step pipelines.
Are Sampling and Structured Output the same thing?
No. Sampling is inference; Structured Output is inference. They are related but address different parts of the AI stack.