Like from a simple "these are probability based word machines" standpoint, an LLM is still pretty likely to give similar responses, and responses that are the same *in substance* in response to a question. But if the LLM is trained to inject randomness that fluctuates MoM...

... that seems to me like it's going to lead searchers to the absolute rock bottom of the internet by mid-2026. This thread is half SEO primal scream, half "how do we live in a society with no shared reality" primal scream

As an SEO if I am told that LLM search is going to be disincentivized from showing my domain repetitively to searchers.... then I simply don't care. Subject me to the winds of fate at that point. I'm sticking to what I can do that will bring reliable, measurable volumes of traffic to my pages.

Why would I bust my ass trying to get perplexity senpai to notice me in April of 2026 when I could be working on long tail pages that already bring in 4x the traffic?

I *think* the function they are talking about in this post is something like "Temperature" as described here. A commenter explains it in this thread as "If the temperature is set appropriately, this results in more varied writing without going off the rails"

The problem with this is, of course, when we are talking about a function where people are trying to understand a condition within reality like "Are vaccines safe", a topic with a lot of quantity of writing about "no they aren't" with zero medical authority but topic domination on sheer volume.

But if we're incentivizing LLMs to be quirky girls, like, this is so unhelpful from a basic utility standpoint. It's very funny how often posts about LLMs include statements like this that make the technology fundamentally unworkable, and just breeze past that lol

Yeah, it relates back to simulated annealing, which has been a technique in AI and ML models for decades; the idea is that if an optimizing model uses strict and deterministic descent to come to a result, it can get stuck in local minima.

But it's an art, and specific to both the model and problem. Not enough heat, and you get a bad answer; too much and you get nonsense. I don't know that you can pick a single value for all LLM queries - if for no other reason than you have no idea what the shape of the surface is.

And I think this goes back to using an LLM for a sum of general knowledge search engine. With those volumes and the training you're able to do on it, an incentive or temp function that results in 70-90% domain drift over 6 months is... wild

I don't make the rules. (But I don't disagree.) I think the thing is... with deterministic or semi-deterministic ranking algorithms, even if you don't know the secret sauce, you can still understand them. A stochastic system is stochastic.

Yeah! You've just summarised my thread so well with this, haha