Whereas constructing my very own LLM-based software, I discovered many immediate engineering guides, however few equal guides for figuring out the temperature setting.
In fact, temperature is a straightforward numerical worth whereas prompts can get mindblowingly advanced, so it could really feel trivial as a product determination. Nonetheless, selecting the best temperature can dramatically change the character of your outputs, and anybody constructing a production-quality LLM software ought to select temperature values with intention.
On this submit, we’ll discover what temperature is and the mathematics behind it, potential product implications, and the way to decide on the correct temperature on your LLM software and consider it. On the finish, I hope that you just’ll have a transparent plan of action to search out the correct temperature for each LLM use case.
What’s temperature?
Temperature is a quantity that controls the randomness of an LLM’s outputs. Most APIs restrict the worth to be from 0 to 1 or some related vary to maintain the outputs in semantically coherent bounds.
From OpenAI’s documentation:
“Larger values like 0.8 will make the output extra random, whereas decrease values like 0.2 will make it extra targeted and deterministic.”
Intuitively, it’s like a dial that may modify how “explorative” or “conservative” the mannequin is when it spits out a solution.
What do these temperature values imply?
Personally, I discover the mathematics behind the temperature area very fascinating, so I’ll dive into it. However if you happen to’re already accustomed to the innards of LLMs otherwise you’re not eager about them, be at liberty to skip this part.
You in all probability know that an LLM generates textual content by predicting the following token after a given sequence of tokens. In its prediction course of, it assigns chances to all attainable tokens that would come subsequent. For instance, if the sequence handed to the LLM is “The giraffe ran over to the…”, it’d assign excessive chances to phrases like “tree” or “fence” and decrease chances to phrases like “house” or “guide”.
However let’s again up a bit. How do these chances come to be?
These chances often come from uncooked scores, referred to as logits, which are the outcomes of many, many neural community calculations and different Machine Learning methods. These logits are gold; they include all the dear details about what tokens could possibly be chosen subsequent. However the issue with these logits is that they don’t match the definition of a chance: they are often any quantity, optimistic or unfavourable, like 2, or -3.65, or 20. They’re not essentially between 0 and 1, they usually don’t essentially all add as much as 1 like a pleasant chance distribution.
So, to make these logits usable, we have to use a operate to rework them right into a clear chance distribution. The operate sometimes used right here known as the softmax, and it’s primarily a chic equation that does two essential issues:
- It turns all of the logits into optimistic numbers.
- It scales the logits so that they add as much as 1.
The softmax operate works by taking every logit, elevating e (round 2.718) to the ability of that logit, after which dividing by the sum of all these exponentials. So the best logit will nonetheless get the best numerator, which implies it will get the best chance. However different tokens, even with unfavourable logit values, will nonetheless get an opportunity.
Now right here’s the place Temperature is available in: temperature modifies the logits earlier than making use of softmax. The formulation for softmax with temperature is:

When the temperature is low, dividing the logits by T makes the values bigger/extra unfold out. Then the exponentiation would make the best worth a lot bigger than the others, making the chance distribution extra uneven. The mannequin would have a better likelihood of selecting essentially the most possible token, leading to a extra deterministic output.
When the temperature is excessive, dividing the logits by T makes all of the values smaller/nearer collectively, spreading out the chance distribution extra evenly. This implies the mannequin is extra more likely to choose much less possible tokens, growing randomness.
How to decide on temperature
In fact, the easiest way to decide on a temperature is to mess around with it. I consider any temperature, like all immediate, needs to be substantiated with instance runs and evaluated towards different potentialities. We’ll talk about that within the subsequent part.
However earlier than we dive into that, I need to spotlight that temperature is an important product determination, one that may considerably affect consumer habits. It might appear fairly easy to decide on: decrease for extra accuracy-based functions, larger for extra inventive functions. However there are tradeoffs in each instructions with downstream penalties for consumer belief and utilization patterns. Listed here are some subtleties that come to thoughts:
- Low temperatures could make the product really feel authoritative. Extra deterministic outputs can create the phantasm of experience and foster consumer belief. Nevertheless, this will additionally result in gullible customers. If responses are all the time assured, customers would possibly cease critically evaluating the AI’s outputs and simply blindly belief them, even when they’re fallacious.
- Low temperatures can cut back determination fatigue. For those who see one robust reply as a substitute of many choices, you’re extra more likely to take motion with out overthinking. This would possibly result in simpler onboarding or decrease cognitive load whereas utilizing the product. Inversely, excessive temperatures might create extra determination fatigue and result in churn.
- Excessive temperatures can encourage consumer engagement. The unpredictability of excessive temperatures can maintain customers curious (like variable rewards), resulting in longer periods or elevated interactions. Inversely, low temperatures would possibly create stagnant consumer experiences that bore customers.
- Temperature can have an effect on the best way customers refine their prompts. When solutions are sudden with excessive temperatures, customers may be pushed to make clear their prompts. However with low temperatures, customers could also be pressured to add extra element or broaden on their prompts with the intention to get new solutions.
These are broad generalizations, and naturally there are a lot of extra nuances with each particular software. However in most functions, the temperature is usually a highly effective variable to regulate in A/B testing, one thing to contemplate alongside your prompts.
Evaluating completely different temperatures
As builders, we’re used to unit testing: defining a set of inputs, operating these inputs by a operate, and getting a set of anticipated outputs. We sleep soundly at night time once we be sure that our code is doing what we anticipate it to do and that our logic is satisfying some clear-cut constraints.
The promptfoo package deal enables you to carry out the LLM-prompt equal of unit testing, however there’s some further nuance. As a result of LLM outputs are non-deterministic and sometimes designed to do extra inventive duties than strictly logical ones, it may be laborious to outline what an “anticipated output” appears like.
Defining your “anticipated output”
The best analysis tactic is to have a human fee how good they suppose some output is, in line with some rubric. For outputs the place you’re on the lookout for a sure “vibe” that you could’t categorical in phrases, it will in all probability be the best technique.
One other easy analysis tactic is to make use of deterministic metrics — these are issues like “does the output include a sure string?” or “is the output legitimate json?” or “does the output fulfill this javascript expression?”. In case your anticipated output will be expressed in these methods, promptfoo has your back.
A extra fascinating, AI-age analysis tactic is to make use of LLM-graded checks. These primarily use LLMs to guage your LLM-generated outputs, and will be fairly efficient if used correctly. Promptfoo gives these model-graded metrics in a number of types. The entire listing is here, and it accommodates assertions from “is the output related to the unique question?” to “evaluate the completely different check instances and inform me which one is finest!” to “the place does this output rank on this rubric I outlined?”.
Instance
Let’s say I’m making a consumer-facing software that comes up with inventive reward concepts and I need to empirically decide what temperature I ought to use with my major immediate.
I’d need to consider metrics like relevance, originality, and feasibility inside a sure funds and guarantee that I’m choosing the right temperature to optimize these components. If I’m evaluating GPT 4o-mini’s efficiency with temperatures of 0 vs. 1, my check file would possibly begin like this:
suppliers:
 - id: openai:gpt-4o-mini
   label: openai-gpt-4o-mini-lowtemp
   config:
     temperature: 0
 - id: openai:gpt-4o-mini
   label: openai-gpt-4o-mini-hightemp
   config:
     temperature: 1
prompts:
 - "Provide you with a one-sentence inventive reward concept for an individual who's {{persona}}. It ought to value below {{funds}}."assessments:
 - description: "Mary - attainable, below funds, unique"
   vars:
     persona: "a 40 yr outdated girl who loves pure wine and performs pickleball"
     funds: "$100"
   assert:
     - sort: g-eval
       worth:
         - "Verify if the reward is definitely attainable and cheap"
         - "Verify if the reward is probably going below $100"
         - "Verify if the reward can be thought of unique by the typical American grownup"
 - description: "Sean - reply relevance"
   vars:
     persona: "a 25 yr outdated man who rock climbs, goes to raves, and lives in Hayes Valley"
     funds: "$50"
   assert:
     - sort: answer-relevance
       threshold: 0.7
I’ll in all probability need to run the check instances repeatedly to check the consequences of temperature modifications throughout a number of same-input runs. In that case, I’d use the repeat param like:
promptfoo eval --repeat 3

Conclusion
Temperature is a straightforward numerical parameter, however don’t be deceived by its simplicity: it will possibly have far-reaching implications for any LLM software.
Tuning it good is vital to getting the habits you need — too low, and your mannequin performs it too secure; too excessive, and it begins spouting unpredictable responses. With instruments like promptfoo, you’ll be able to systematically check completely different settings and discover your Goldilocks zone — not too chilly, not too sizzling, however good. ️
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