Temperature

A practical guide to understanding what temperature means in AI and how it affects output quality.

What temperature controls in AI models, how it affects the range and consistency of responses, and when to adjust it for different product use cases.

22 May 20264 min read

What it is

Temperature is a setting that controls how much randomness is introduced when an glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term generates a glossaryResponseA response is the data or result returned by a server after receiving a request.Open glossary term.

At low temperature, the glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term consistently selects the most probable next token, producing glossaryResponseA response is the data or result returned by a server after receiving a request.Open glossary term that are predictable, focused, and deterministic. Ask the same question twice and you will get the same or very similar answers.

At high temperature, the glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term introduces more randomness, selecting from a broader range of possible tokens. This produces more varied, creative, and sometimes surprising glossaryResponseA response is the data or result returned by a server after receiving a request.Open glossary term — but also less consistent and potentially less accurate ones.

Temperature is typically set on a scale from zero to one or zero to two, depending on the glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term. A temperature of zero means fully deterministic outputs. Higher values introduce increasing levels of randomness.

Understanding temperature helps you configure AI glossaryFeatureA feature is a specific piece of functionality within a product that delivers value to users. It represents something users can do or experience as part of the overall product.Open glossary term for their specific purpose — precision where accuracy matters, creativity where variation adds value.

When to use it

Understand when temperature adjustment is relevant. Low temperature is most appropriate when:

High temperature is most appropriate when:

Accuracy and consistency are critical

The AI is performing factual retrieval, summarisation, or structured tasks

Users expect the same query to return the same result

The output will be used in a consequential context

Creative variation is desirable

You are generating multiple options for the user to choose from

The task benefits from exploring a broader range of possibilities

Consistency matters less than novelty

Key takeaway

Match the temperature to the task. Precision tasks need low temperature. Creative tasks benefit from higher temperature.

How it works

Understand the basic mechanism. When generating a glossaryResponseA response is the data or result returned by a server after receiving a request.Open glossary term, the glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term produces a probability distribution over all possible next tokens. Temperature scales this distribution before the model samples from it.

Low temperature sharpens the distribution, making high-probability tokens more likely to be selected. High temperature flattens it, giving lower-probability tokens a greater chance of being chosen.

This is why high temperature produces more diverse and unexpected outputs — the glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term is drawing from a wider range of possibilities rather than consistently choosing the most likely option.

What this means for designers and product teams. Temperature is a configuration decision with real product implications. A creative writing tool set to low temperature will feel formulaic. A customer support bot set to high temperature will give inconsistent and potentially unreliable answers.

In practice, most use cases work well with a moderate temperature in the range of 0.3 to 0.7. Extremes are only appropriate in specific glossaryContextThe surrounding conditions that shape behaviour and decisions.Open glossary term.

What to look for

Focus on:

Output consistency — whether the model produces appropriately similar results to similar inputs

Creative variation — whether the outputs feel varied and interesting where that is the goal

Accuracy — whether high temperature is introducing errors or hallucinations

User experience — whether the level of variation matches user expectations

Edge cases — whether high temperature produces any problematic outputs

Where it goes wrong

Most issues come from: Using default temperature settings without considering whether they match the task is the most common mistake.

Using high temperature for factual or structured tasks where consistency matters

Using low temperature for creative tasks where variation is the whole point

Not testing across the full range of inputs at the chosen temperature

Assuming temperature is the primary lever for output quality when prompt design matters more

What you get from it

Understanding temperature gives you:

A practical configuration lever for improving AI output quality

Better ability to match AI behaviour to specific product use cases

Clearer criteria for evaluating output consistency and creativity

More informed conversations with engineers about model configuration

Key takeaway

Temperature is a small setting with a significant impact on user experience. It should be set deliberately for each use case, not left at default.

FAQ

Common questions

A few practical answers to the questions that usually come up around this method.

What is temperature in AI?

Temperature is a setting that controls how random or predictable an glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term's outputs are. Low temperature produces consistent, focused glossaryResponseA response is the data or result returned by a server after receiving a request.Open glossary term. High temperature introduces more variation and creativity. It is typically set on a scale from zero to one or two.

What temperature should I use for my AI product?

It depends on the task. For factual, structured, or customer-facing tasks where glossaryConsistencyConsistency is the use of uniform patterns, behaviours, and visual elements across a product to create familiarity and predictability. It helps users learn once and apply that knowledge throughout the experience.Open glossary term matters, use a lower temperature — typically between 0.1 and 0.4. For creative tasks where variation is valuable, a higher setting — 0.7 to 1.0 — works better. Test with your specific use case rather than relying on defaults.

Does high temperature cause hallucinations?

It can increase them. Higher temperature encourages the glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term to draw from a broader range of possibilities, which can glossaryLeadA lead is a potential customer who has shown interest in a product or service, typically by providing contact information or engaging with content.Open glossary term to less accurate or more fabricated glossaryResponseA response is the data or result returned by a server after receiving a request.Open glossary term. For tasks where accuracy is important, lower temperature reduces this risk.

Is temperature the most important setting to get right?

No. glossaryPromptA prompt is the input or instruction given to an AI system to guide its output or response.Open glossary term design typically has a greater impact on glossaryOutput QualityHow accurate, useful, and relevant a result is.Open glossary term than temperature. Temperature should be considered once the prompts are working well, not as a substitute for good guidePrompt EngineeringWhat prompt engineering involves, how it shapes AI output quality, and what product and design teams need to know to do it well.Open guide.

Can temperature be changed after launch?

Yes. Temperature can be adjusted at any point. If you find your AI glossaryFeatureA feature is a specific piece of functionality within a product that delivers value to users. It represents something users can do or experience as part of the overall product.Open glossary term is too inconsistent or too predictable in production, adjusting temperature is a quick and low-risk change to try. Always test the impact before rolling changes out broadly.

Quick take

Temperature controls how creative or predictable an AI response is — and choosing the right setting is a product decision, not just a technical one.

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