> ## Documentation Index
> Fetch the complete documentation index at: https://handbook.sutro.sh/llms.txt
> Use this file to discover all available pages before exploring further.

# Don't be fooled by determinism

> Why absolute determinism is less useful than measured consistency for real-world analytical AI systems.

## Don't be fooled by determinism

A lot of research goes into getting models to behave completely deterministically, all the way down to hardware cycles on GPU. This may be useful in academic and low-level research settings where experimental reproducibility is a necessity. But in applied AI, absolute determinism from models isn't very useful. In fact, it's probably damaging to overall result quality. Why?

It's not useful because foundation models receive [unstructured data from an unbounded, infinite range](/patterns/evals/static-evals-vs-judges). Even if you're able to get what appears to be absolutely consistent behavior from a model on a certain input, it's possible that some extremely subtle variation of that input - even so much as an extra comma, misspelling, or word rearrangement - can result in a different output. This is the practicality of real-world data. It's messy, and will constantly surprise you in new ways.

It can be damaging because *some* creativity can help with reasoning. Teams often set model [temperature](/patterns/consistency/temperature) to 0 in the hopes of increasing consistency, but they're somewhat hamstringing a model's ability to think about the task. It's like putting handcuffs on someone trying to complete a jigsaw puzzle. Maybe they can get it done, but they'll be pretty limited in their available range.