Comparison
Greedy Decoding vs Sampling
Greedy Decoding and Sampling are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Greedy Decoding
Greedy Decoding comes up when the question is fundamentally about inference.
Asking a model "What is 2+2?" — greedy is fine.
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.
Frequently asked
What is the difference between Greedy Decoding and Sampling?
Greedy Decoding: Greedy decoding always picks the single highest-probability next token. It is deterministic, fast, and often dull. 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.
When should I use Greedy Decoding vs Sampling?
Greedy Decoding is the right concept when you are focused on inference. Sampling applies when you are focused on inference.
Are Greedy Decoding and Sampling the same thing?
No. Greedy Decoding is inference; Sampling is inference. They are related but address different parts of the AI stack.