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Understanding the role of temperature settings in AI output
Learn how adjusting the temperature settings in AI tools can help you strike a balance between reliability and innovation.
Whether the ideal output is deterministic or imaginative, temperature settings control how AI tools respond to prompts.
An AI tool's temperature setting defines the predictability of its output. Higher temperatures yield more creative results, while lower temperatures produce more predictable responses. How much a user can change temperature depends on the tool: Some tools include the option to adjust temperature as part of the standard UI, others offer it through an API, and others don't let users edit temperature at all.
To properly integrate AI into workflows, organizations should start by defining their end goal, such as generating creative content or summarizing existing information. Aligning the AI temperature setting with that goal is crucial to achieving the desired results. Understanding what to look for and how to adjust settings can help teams ensure that model output is relevant and useful.
What are AI temperature settings?
To create output, large language models predict the next chunk of text, called a token, and assign a probability or confidence level to each predicted token. The model's temperature dictates the predictability of the tokens used in model output.
Models at the low end of their temperature scale will choose the highest-probability words, leading to more formal and predictable output. As the temperature increases, the AI tool has the latitude to pick less predictable tokens, leading to more creative and random responses, but also more frequent hallucinations.
AI temperature settings typically range from 0 to 1, with some models reaching up to 2. A setting nearer to 0 reduces the model's creativity, while higher temperatures increase its ability to generate more random results.
| Temperature setting | Range | Characteristics | Use cases |
| Low | 0.1-0.5 | High reliability and accuracy | Noncreative tasks such as summarizing data |
| Medium | 0.5-1.0 | Balance between creativity and predictability; often the default setting | Standard tasks where some variability is desirable, such as coding and business writing |
| High | 1.0-2.0 | High creativity with increased risk of hallucinations | Creative tasks such as brainstorming, fictional content generation and image generation |
How to adjust AI temperature
The ability to adjust temperature settings varies across AI tools. Some expose this feature directly to users, whereas others treat it as an advanced functionality.
For example, because Perplexity AI focuses on accurate search results, it does not provide a temperature setting option. Similarly, OpenAI's ChatGPT does not offer a user-modifiable temperature setting in its standard web UI; its default value is approximately 0.7-0.8.
However, many AI tools enable temperature adjustment through their APIs. For example, both ChatGPT and Anthropic's Claude offer API-based temperature settings. Google's Gemini also lets developers modify temperature for larger-scale applications; enterprise customers can configure temperature settings in the Vertex AI Gemini API configuration file.
Other tools offer temperature settings in more user-friendly formats. For example, GitHub Copilot lets users adjust response modes to modify the temperature.
If adjusting temperature settings directly isn't an option, an alternative approach is to test the same prompt on different AI tools and compare the outputs. Teams can also experiment with prompting for specific tones or levels of creativity to indirectly adjust response randomness and uniqueness.
Tips for AI tool implementation
Achieving your desired output with AI hinges on selecting the right tool and using the right language.
There are many business use cases for AI, from creating images to summarizing scientific documents. Accordingly, vendors often develop tools for specific use cases, such as image generation, coding, application development or AI-powered search.
Once a team has chosen the appropriate tool for their use case, they must enter targeted prompts. Best practices for prompt engineering include the following:
- Provide specific and clear instructions featuring relevant keywords.
- Define the desired output format.
- Use iterative prompts and refine them as needed.
- Frame prompts in a positive manner, rather than telling the tool what not to do.
When to adjust AI temperature
Adjusting AI temperature requires some experimentation, always keeping the end goal in mind.
Tasks that warrant a lower temperature setting are usually deterministic, where results must remain well within the project context. Examples include the following:
- Language translation.
- Format conversion.
- Syntax and grammar evaluation.
Tasks that benefit from higher temperature settings usually involve more creativity and flexibility, such as the following:
- Fictional content creation.
- Marketing materials development.
- Brainstorming and idea generation.
However, the increased creativity of higher temperature settings can lead to hallucinations or even nonsensical responses. Be sure to thoroughly read and edit AI output produced using higher temperature settings to avoid hallucinations, unprofessional language and other undesirable results.
Damon Garn owns Cogspinner Coaction and provides freelance IT writing and editing services. He has written multiple CompTIA study guides, including the Linux+, Cloud Essentials+ and Server+ guides, and contributes extensively to Informa TechTarget, The New Stack and CompTIA Blogs.
