Anne Hathaway received the same AI-written thank you note from every candidate—and Meryl Streep said what every boss is thinking: “That’s just tragic.”
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.
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.
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.
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.