Comparison
Greedy Decoding vs Temperature
Greedy Decoding and Temperature 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 Temperature
Low for code/extraction; medium for chat; high for creative writing.
Temperature 0: same prompt, same response, every time.
Frequently asked
What is the difference between Greedy Decoding and Temperature?
Greedy Decoding: Greedy decoding always picks the single highest-probability next token. It is deterministic, fast, and often dull. Temperature: Temperature is a generation parameter that controls randomness. 0 is deterministic (always pick the most likely token); higher values produce more diverse, surprising output.
When should I use Greedy Decoding vs Temperature?
Greedy Decoding is the right concept when you are focused on inference. Low for code/extraction; medium for chat; high for creative writing.
Are Greedy Decoding and Temperature the same thing?
No. Greedy Decoding is inference; Temperature is inference. They are related but address different parts of the AI stack.