What is Temperature? - AI Glossary

Temperature is a hyperparameter used during the text generation process that influences the probability distribution of the next potential tokens, thereby controlling the level of randomness and creativity in the output. A higher temperature results in more random and potentially creative output, while a lower temperature makes the output more deterministic, focused, and less surprising.

The Predictable Machine: How AI Chooses Words (The Basics)

Before we dive into temperature, let’s quickly revisit how AI language models generate text in the first place. As we discussed regarding tokens (the AI’s basic text units), these models work by predicting the most likely next token based on the sequence of tokens they have seen so far (your prompt and the text they have already generated).

When an AI model is about to generate the very first token of its response, it looks at your entire prompt (converted into tokens). Based on its training data – which is billions or trillions of words from the internet, books, code, etc. – it calculates a probability for every single token in its vocabulary, predicting how likely each one is to come next given your prompt.

Imagine the AI is presented with the text “The cat sat on the…”. Based on its training, it calculates probabilities for the next token:

“mat”: Probability 10%
“floor”: Probability 8%
“couch”: Probability 7%
“rug”: Probability 6%
“chair”: Probability 5%
“dog”: Probability 0.5%
“sky”: Probability 0.0001%
… and so on, for every token in its vocabulary (which could be tens or hundreds of thousands).

Without any controls like temperature, the simplest way for the AI to generate text would be to just pick the token with the highest probability every single time. In our example, it would always pick “mat”.

If the AI always picked the most probable token, its output would be very predictable, repetitive, and arguably quite boring. Every time you gave it the same prompt, you’d get the exact same response. This is where temperature comes in.

Introducing Temperature for Creativity

Temperature is applied after the model calculates these raw probabilities for the next token, but before it actually selects the next token. It’s a way to re-shape or re-sculpt the probability distribution.

Think of the probabilities like peaks on a landscape map. The highest peaks are the most probable tokens, the lower peaks are less probable.

Low Temperature: Imagine a very cold environment. Everything is frozen in place, rigid. The highest peaks stand out even more sharply compared to the lower ones. The AI becomes very likely to pick from the absolute highest probability tokens, almost ignoring the rest.
High Temperature: Imagine a hot environment. The heat causes things to spread out, to become more fluid and dynamic. The differences between the probability peaks become less dramatic. The AI is more likely to pick from lower probability tokens that it would normally ignore.

The effect of temperature is this:

Lower Temperature values (closer to 0): Make the distribution sharper. The probability gap between the most likely token and the less likely tokens becomes wider. This heavily favors picking the single most likely token.
Higher Temperature values (greater than 0): Make the distribution flatter. The probability gap between the most likely token and the less likely tokens becomes narrower. This gives less likely tokens a greater chance of being selected.

Temperature is typically a non-negative value, often set between 0.0 and 2.0, though some interfaces might allow higher values. A common default value is often around 0.7 or 1.0, aiming for a balance.

Setting the temperature parameter is like telling the AI: “Okay, you’ve figured out the probabilities for the next token. Now, how adventurous should you be in choosing one? Should you play it safe and stick to the most obvious choices (low temperature), or should you be willing to take a chance on slightly less obvious, potentially more interesting options (high temperature)?”

What Outputs Look Like at Different Temperatures

Let’s explore how different temperature settings can dramatically change the AI’s output for the same input prompt.

Imagine our prompt is: “Write a short, creative sentence about a journey.”

Temperature = 0.0 (The Cold, Hard Facts)

How it works: When temperature is set to 0, it’s often called “greedy” sampling. The AI simply picks the single token with the highest probability at each step. It’s the most deterministic setting.
Output Characteristics: Highly predictable, repeatable (giving the same prompt often yields the exact same response), focused, factual, less prone to error or “hallucination” (making things up), but can be repetitive or lack flair.
Example Output: “The traveler walked down the road.” (Very standard, safe prediction).
Pros: Reliability, consistency, good for tasks where correctness and predictability are key (summarizing text, answering factual questions, generating code, following strict instructions).
Cons: Lacks creativity, can be repetitive, might get “stuck” in loops, outputs can feel generic.
Human Nuance: Like a human strictly adhering to rules or recalling a memorized fact without adding any personal interpretation or flourish.

Low Temperature (e.g., 0.1 – 0.5): Focused and Reliable with a Hint of Variation

How it works: The probability distribution is still sharp, heavily favoring the most likely tokens, but slightly less so than at 0. There’s a tiny chance that a token that isn’t the absolute top choice might be selected.
Output Characteristics: Mostly predictable and reliable, outputs will be very similar for the same prompt but might have minor variations. Still factual and focused but with a touch more natural language flow than temperature 0.
Example Output: “The adventurer embarked on a long trek.” (Similar meaning to temperature 0, but uses slightly different, still highly probable words).
Pros: Combines reliability with a bit more natural variation, good for tasks requiring mostly factual information but where exact phrasing can differ. Less likely to produce completely nonsensical output than high temperature.
Cons: Still not very creative, outputs can be quite similar across multiple generations.
Human Nuance: Like a human answering a question factually but choosing slightly different wording each time, or sticking closely to a script but allowing minor ad-libs.

Medium Temperature (e.g., 0.6 – 1.0): The Creative Sweet Spot

How it works: The probability distribution is flattened moderately. Less likely tokens have a significantly better chance of being selected compared to low temperature settings. The AI is encouraged to explore a wider range of potential next words.
Output Characteristics: Balanced creativity and coherence. Outputs are less predictable and will vary noticeably with each generation. Good for generating ideas, creative writing, and different phrasing options. Still generally coherent but might occasionally produce slightly unexpected combinations.
Example Output Options (different generations):
- “Through swirling mist, the voyager sought the ancient peak.”
- “A stardust journey began, mapless and bold.”
- “They set forth, not on a path, but on a feeling of distant shores.”
Pros: Generates diverse and often creative outputs, excellent for brainstorming, writing stories, poems, marketing copy, or anything requiring imagination.
Cons: Outputs are not repeatable, might occasionally deviate slightly from the prompt’s strict intent, small chance of minor inaccuracies or awkward phrasing compared to low temp.
Human Nuance: Like a human brainstorming ideas freely, writing creatively, or engaging in casual conversation where unexpected topics might arise. This range often feels most “human-like” in terms of varied expression.

High Temperature (e.g., 1.1 – 2.0+): Wild and Unpredictable

How it works: The probability distribution is significantly flattened. Even tokens with very low probabilities have a realistic chance of being selected. The AI is highly encouraged to pick unusual or unexpected tokens.
Output Characteristics: Highly random, creative, surprising, often nonsensical, incoherent, or completely off-topic. Can produce interesting or bizarre combinations, but frequently sacrifices coherence and accuracy. Outputs will be drastically different with each generation.
Example Output Options (different generations):
- “Crimson whispers navigated the silence of the cheese-grater moon.” (Very creative, but makes little literal sense).
- “Journey spoke in whispers while clocks melted sideways towards Tuesday.” (Abstract, possibly poetic but nonsensical).
- “The blue idea traveled through the concept of socks.” (Completely abstract).
Pros: Can be useful for generating extremely diverse ideas, avant-garde text, or exploring the very edges of the model’s learned patterns. Might produce a truly novel phrase occasionally.
Cons: Very high chance of producing irrelevant, incoherent, or factually incorrect text. Requires significant filtering and editing. Not suitable for tasks requiring accuracy or clear communication.
Human Nuance: Like a human free-associating, dreaming, or perhaps speaking nonsense words – highly creative but detached from logical constraints.

How Temperature Rescales Probabilities

To get slightly more technical, let’s quickly look at how temperature achieves this probability reshaping. Don’t worry, we’ll keep it simple, no complex math formulas!

When an AI model processes information to predict the next token, it calculates a raw score for each possible token in its vocabulary. These raw scores are often called “logits.” Higher logits mean the model thinks that token is more appropriate or likely in the context.

Before converting these logits into final probabilities that sum up to 100% (using a function called “softmax”), the temperature parameter is applied. The raw logits are divided by the temperature value.

If Temperature is High (> 1): Dividing the logits by a number greater than 1 makes the resulting numbers smaller. When these smaller numbers are converted into probabilities, the differences between the probabilities are reduced, leading to a flatter distribution where low probability tokens get a relative boost.
If Temperature is Low (< 1): Dividing the logits by a number less than 1 (but greater than 0) makes the resulting numbers larger. When these larger numbers are converted into probabilities, the differences between the probabilities are exaggerated, leading to a sharper distribution where the highest probability tokens become even more dominant.
If Temperature is 1: Dividing by 1 doesn’t change the logits. The probabilities are calculated directly from the original logits without any temperature modification. This is often considered the “neutral” setting.

So, temperature acts as a scaling factor on the raw scores before they are turned into the final probabilities used for sampling the next token. This simple division step is the core mechanism behind its powerful effect on output randomness.

Beyond Temperature: Other Controls for Randomness (Briefly)

While temperature is a primary control, it’s worth mentioning that other techniques are often used alongside or instead of pure temperature sampling to control the generation process and introduce variation in a more controlled way.

Top-k Sampling: Instead of considering all possible tokens, the model only considers the top k most probable tokens at each step. The probability distribution is then re-normalized only across these top k tokens, and the next token is sampled from this reduced set.
Top-p (Nucleus) Sampling: This method considers tokens whose cumulative probability exceeds a threshold p. For example, if p=0.9, the model considers the smallest set of most probable tokens whose probabilities add up to at least 90%. The next token is then sampled from this set. This adapts dynamically to the probability distribution – if one token is highly probable, the set might only contain that one; if probabilities are spread out, the set will be larger.

Temperature, Top-k, and Top-p sampling are often used together to fine-tune the balance between randomness and coherence. For example, you might use a certain temperature and Top-p sampling simultaneously. Researchers and developers choose combinations of these techniques based on their goals for the AI’s output style.

Research and Statistics on Temperature

Temperature is not just a simple slider in a user interface; it’s a parameter actively studied in AI research to understand its effects on the quality, diversity, and other characteristics of generated text. Finding universal statistics about temperature values themselves is less common than finding research that uses temperature as an experimental variable and measures the results.

Here’s what research often explores regarding temperature:

Diversity of Output: Studies consistently show that increasing temperature leads to significantly higher diversity in the generated text. Metrics like the number of unique words or n-grams (sequences of n words/tokens) per generated passage are used to quantify this. A higher temperature allows the model to explore more of its vocabulary and produce less repetitive phrasing across multiple generations for the same prompt.
Coherence and Quality: While higher temperatures increase creativity, research often finds a trade-off with coherence and factual accuracy. Very high temperatures can lead to outputs that are rambling, nonsensical, or deviate factually from the input or common knowledge. Lower temperatures tend to produce more structured and factually grounded text, assuming the underlying model was trained correctly. Researchers evaluate this using both automated metrics (less reliable for creative text) and human evaluation.
Task Performance: The optimal temperature varies greatly depending on the specific task. Research evaluating LLMs on tasks like summarization, question answering, translation, and creative story generation uses temperature as a parameter to see which setting yields the best performance according to task-specific evaluation criteria. For instance, studies on factual question answering would likely find lower temperatures perform better, while studies on poetry generation would favor higher temperatures.
Exploring Model Capabilities: Researchers use temperature to probe what kinds of outputs a model is capable of generating. By setting a very high temperature, they can see the less probable associations the model has learned, which can sometimes reveal biases or unexpected patterns in the training data.
Exposing the Parameter: Major AI companies that provide access to their models through APIs or interfaces expose the temperature parameter, allowing developers and users to control this crucial aspect of text generation.

These research findings highlight that temperature is a powerful lever for controlling AI output characteristics, and its effect is a key consideration in both developing and using language models effectively.

How to Choose the Right Temperature

Since temperature is a knob you can control, how do you decide what setting to use? The best value depends entirely on what you are trying to achieve with the AI.

Here’s a simple guide based on typical use cases:

For reliable, factual, or consistent output (e.g., summarizing, extracting info, answering specific questions, generating code, structured data): Use a low temperature (0.0 to 0.5). Start at 0.0 for maximum predictability. If the output feels too stiff or repetitive, incrementally increase it to 0.1, 0.2, etc., while checking that it maintains accuracy.
For creative writing, brainstorming, generating variations, or dialogue: Use a medium temperature (0.6 to 1.0). This range offers a good balance between creativity and coherence. Experiment within this range – higher values might lead to more surprising twists, lower values to more grounded creativity.
For generating highly diverse options, abstract ideas, or artistic/experimental text (be prepared to filter): Use a high temperature (above 1.0, maybe up to 1.5 or 2.0). This is like throwing ideas at the wall to see what sticks. Most outputs might be unusable, but you might get a truly unique gem. Use this sparingly and with caution.

Important Tip: When experimenting, it’s often best to change only one parameter at a time (like temperature) while keeping others constant (like the prompt, or whether you are using Top-k/Top-p if those options are available) so you can clearly see the effect of that single change.

Remember that the “perfect” temperature is subjective and task-dependent. What works well for generating a sci-fi short story might be terrible for writing a product description. Don’t be afraid to play around with the setting in the AI tools you use!

Conclusion

Understanding temperature is essential for anyone using or developing with AI language models because it directly controls the balance between:

Predictability vs. Surprise: Will the AI give you the most probable answer, or explore less common possibilities?
Focus vs. Diversity: Will the output stick closely to the main point, or branch out into varied ideas?
Reliability vs. Creativity: Is the priority factual correctness and consistency, or novel and imaginative text?

By adjusting the temperature knob, you can steer the AI towards generating text that is highly reliable and factual (low temperature) or wildly creative and unpredictable (high temperature), or find a comfortable balance in between (medium temperature).

While temperature is a statistical control and doesn’t replicate human consciousness or creativity, it is a powerful tool that allows us to unlock different capabilities of AI language models for a diverse range of applications.