---
language: en
title: "Temperature in LLMs"
description: "Temperature in LLMs adjusts randomness during generation. Learn how to compare text variations and why the parameter does not guarantee factual accuracy."
canonical: "https://tropk.ai/en/resources/glossary/temperature-in-llms"
last_modified: "2026-10-12T16:13:20.113Z"
---

# Temperature in LLMs

Reviewed on 2026-10-06.

## What it means

Temperature in LLMs is a generation parameter that modifies the probabilities used to select the next token. When sampling is used, lower values tend to concentrate choices, while higher values allow more variation. The implementation and accepted range depend on the system. The parameter acts during response generation and does not represent a new model training stage.

## Why it matters for marketing

For marketing, adjusting temperature can help explore variations in wording or seek greater consistency in repeated tasks. The team needs to define what it intends to vary and which information must remain accurate. A more diverse response is not automatically more useful; a response with little variation may also consistently repeat an incorrect claim.

## Example in practice

Imagine a store testing headlines to promote a lighting collection. In the API it uses, the team keeps the model and brief constant and generates sets with two supported temperature values. It compares how often the headlines repeat and how faithfully they reflect the supplied materials and prices. It chooses the setting that offers usable alternatives without increasing unwarranted changes, then submits the options for editorial review.

## How to apply or verify

Check the documentation to see whether the model accepts temperature and whether its generation mode uses sampling. Record the value sent, the model, the version, and other parameters, such as top-p, which also affect choices. Compare several responses to the same set of instructions. Evaluate diversity and accuracy separately, without drawing conclusions from a single output.

## Important distinctions

Temperature does not measure human creativity, quality, or degree of factual confidence. It also does not reveal which values a public service uses internally. In Transformers, the do\_sample parameter distinguishes sampling from greedy generation; therefore, temperature must be interpreted within the chosen strategy. Lowering it is not enough to show that the entire system will produce identical responses in every run.

## Sources and references

- [GenerationConfig: parameters for text generation](<https://huggingface.co/docs/transformers/main/en/main_classes/text_generation>) — Hugging Face
- [Machine Learning Glossary: temperature](<https://developers.google.com/machine-learning/glossary#temperature>) — Google for Developers

## Continue a leitura

- [Inference](</en/resources/glossary/inference>)
- [Non-determinism in LLMs](</en/resources/glossary/non-determinism-in-llms>)
- [Token](</en/resources/glossary/token>)

## About the author

[Vinicius Pacheco](</en/team/vinicius-pacheco>)

Responsible for Tropk’s technical content, focusing on crawling, indexing, structured data, and agent access to content.

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