Temperature
A sampling setting that controls how much randomness goes into choosing each next token.
At temperature 0 the model takes the most likely token every time, so the same input gives close to the same output. As temperature rises, less likely tokens get a real chance, and the text becomes more varied and less predictable.
Use low values - 0 to 0.3 - for extraction, classification, code and anything you will run twice and want to match. Use middle values around 0.7 for drafting and brainstorming. High values above 1 mostly produce noise rather than creativity; if the output feels stale, a better prompt beats a higher temperature.
Temperature interacts with top-p, and changing both at once makes results hard to reason about. Pick one to tune.
Tagged
Where this comes up
Prompts, configs and tutorials in the library that touch this term.
- GuideUniversal
Moving your rules into a config file
How to stop repeating the same instructions in every message, and what belongs in a system prompt against what belongs in the task.
- GuideUniversal
Choosing a model for the job
A practical comparison of what to reach for when, based on the shape of the task rather than benchmark scores.
- Before and afterUniversal
Asking for a summary
The same document, the same model. The weak version returns the article rearranged; the strong one returns something you can act on, because it says…
Tested on Claude Sonnet · May 2026
- CheatsheetChatGPT
ChatGPT commands and keyboard shortcuts
The commands and shortcuts that are not in the interface anywhere, collected in one table. Mostly keyboard, mostly undocumented.
Tested on GPT-5 · May 2026
- PromptUniversal
Strict code review of a diff
A review prompt that reports only what would break in production, gives the smallest fix for each finding, and is allowed to say the diff is fine.
- PromptUniversal
Turn a vague request into a written brief
Takes a one-line request from a colleague or client and returns the questions that have to be answered before work starts.