TechnicalAI glossary
Sampling temperature
The setting that controls how much randomness goes into a model's choice of words. Low, it almost always picks the most likely word: stable, predictable answers. High, it dares less expected choices: more variety, but a greater risk of incoherence. Beware: a low temperature makes answers consistent, not necessarily true.
In other words
A cook's dial between following the recipe to the letter and improvising: reassuring at one end, surprising (for better or worse) at the other.
Alyriaz articles that mention it
- An AI will fly a probe to an asteroid on its own
In 2027, the start-up AstroForge will hand over the piloting of a mining probe to an AI trained on data from thousands of sensors. We explain why, and how it intends to make sure it works.
- Why doesn't the same AI respond differently depending on the app?
The same model produces very different responses depending on the application using it. We explain the five factors behind this gap, and why it's useful to know.
- Can an AI have an idea that no one has had before?
A benchmark published in August 2026 measures models' ability to generate a scientific hypothesis by stripping away anything they might have memorised. The results suggest a real ceiling. We explain the protocol and what it reveals.
- What is an AI's "temperature"? The setting that decides whether it's sensible or inventive
Temperature is the setting that controls the degree of randomness in an AI's responses. We explain how it works, why a low temperature doesn't guarantee truth, and how to choose it based on your use case.