If you've poked around the settings of any AI API or advanced chat interface, you've probably seen two parameters called temperature and top-p sitting there with sliders or number inputs. Most documentation explains them with probability distributions and mathematical formulas, which is accurate but not very useful if you just want to know what they do and how to set them.
This article explains both parameters in plain language, what they actually affect in practice, and how to think about setting them for different kinds of tasks.
What temperature does
Temperature controls how predictable or creative the model's responses are. At low temperature, the model strongly favors the most likely next token at each step, which makes its outputs more predictable, consistent, and focused. At high temperature, the model is more willing to pick less likely tokens, which makes its outputs more varied, creative, and sometimes surprising.
Think of it like this. Imagine the model is choosing the next word in a sentence and it has assigned probabilities to thousands of possible words. At low temperature, the word with the highest probability wins almost every time. At high temperature, words with lower probabilities get a more meaningful chance of being chosen, which leads to more unexpected combinations and directions.
In practice, low temperature (something like 0.1 to 0.4) is good for tasks where you want consistent, accurate, predictable outputs. Factual questions, data extraction, classification, code generation, these all benefit from lower temperature because you want the model to give you the most reliable answer rather than an interesting one.
High temperature (something like 0.7 to 1.0) is better for creative tasks where variety and originality are valuable. Brainstorming, creative writing, generating multiple options to choose from, these benefit from higher temperature because predictable outputs are actually less useful.
A temperature of 0 means the model always picks the most probable token, making responses completely deterministic. The same input will always produce the same output. This is useful for testing and debugging because it removes randomness from the equation entirely.
What top-p does
Top-p, sometimes called nucleus sampling, is a different way of controlling the same general thing. Instead of scaling the probabilities of all possible tokens up or down like temperature does, top-p cuts off the less likely tokens entirely and only samples from the most probable ones.
Specifically, top-p sets a cumulative probability threshold. A top-p of 0.9 means the model only considers tokens until the cumulative probability of the options it's considering reaches 90 percent, and ignores everything below that threshold. A top-p of 0.5 is more restrictive, only considering the most probable tokens that together account for 50 percent of the probability mass.
The practical effect is similar to temperature in that lower values make outputs more focused and predictable, while higher values allow more variety. The difference is in how they achieve that effect, which matters in some edge cases but is often less important than choosing reasonable values for your use case.
According to research from Stanford's AI lab, the combination of temperature and top-p gives developers fine-grained control over the creativity-reliability tradeoff, and the optimal settings vary significantly depending on the task type and the specific model being used.
Should you change both or just one
Most practitioners recommend adjusting one and leaving the other at its default rather than changing both simultaneously. Changing both at the same time makes it harder to understand what's actually affecting your outputs.
The most common approach is to adjust temperature and leave top-p at its default (usually 1.0, meaning no cutoff). Temperature is more intuitive to reason about and its effects are easier to predict, which makes it the better starting point for most people.
If you find that even at low temperature you're occasionally getting outputs that go off in unexpected directions, that's a case where lowering top-p can help by cutting off the tail of less likely tokens entirely.
Practical settings for common tasks
For factual question answering or data extraction, start with a temperature around 0.1 to 0.3. You want the model to be reliable and consistent, not creative.
For writing assistance where you want a natural voice without too much randomness, something in the 0.5 to 0.7 range usually works well. The outputs feel natural without being unpredictable.
For brainstorming, creative writing, or generating multiple options, try 0.8 to 1.0. You want variety and you're willing to accept that some outputs will be better than others.
For code generation, most developers stick to lower temperatures, around 0.2 to 0.4, because code either works or it doesn't and creativity isn't usually what you're after. The exception is when you're asking the model to brainstorm approaches to a problem, where higher temperature can surface options you wouldn't have thought of.
The best way to find the right setting for your specific use case is to run the same prompt several times at different temperature values and compare the outputs. The differences are usually obvious and it doesn't take many iterations to find a range that works for what you're building.