this post was submitted on 20 Jun 2026
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[–] anothermember@feddit.uk 0 points 2 months ago (1 children)

Where was I wrong? I said nothing that contradicts the detail you added.

[–] hperrin@lemmy.ca 0 points 2 months ago* (last edited 2 months ago) (1 children)

LLMs won't spit out the same word-for-word response for the same prompt

You can give an LLM the same seed and it will spit out the same word-for-word response. That’s how they work. It’s just a bunch of math.

[–] anothermember@feddit.uk 0 points 2 months ago (1 children)

You can give an LLM the same seed and it will spit out the same word-for-word response. That’s how they work. It’s just a bunch of math.

You're assuming that because I missed out that detail I must be ignorant of it, that's not very charitable, I could well have been ignorant of it but you could have made your otherwise useful clarification without telling me I was wrong.

[–] hperrin@lemmy.ca 1 points 2 months ago* (last edited 2 months ago)

You said “that’s not how they work”. But that is how they work. Same prompt = same output. Throw some random data in there to jumble things around and you get a little variance. That’s the seed, and we only need to do that because LLMs are inherently deterministic.

Same reason Minecraft has a random seed for world generation, and block cipher algorithms use an initialization vector and/or feedback loop. We don’t want the same thing every time.

I did say that you’re right, because the tooling we use around the LLM itself does exactly what you’re talking about. So, in practice, you’re right.