Maximizing the Potential of LLMs: A Guide to Prompt Engineering

Re:Maximizing the Potential of LLMs: A Guide to Prompt Engin... by usrbinbash on Tuesday April 11, 2023 @10:06AM (#35525358)

Not so much witchcraft, as acknowledging that LLMs are not in fact, as humans are so quick to anthropomorphize, "reasoning", or "thinking" or even "knowing" things, but that they are stochastic sequence completion engines.

Highly sophisticated ones, and useful beyond a doubt, but when all is said and done, this is what they do: They complete sequences.

So coming up with sequences that produce desired results is an important function when considering how to use this tech in products.

Whether we can call this engineering or the tech-version of horse-whispering, is up for debate. But it is acknowledging the modus operandi of the tool at hand, and thus it can help using it to is potential.

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Re:Maximizing the Potential of LLMs: A Guide to Prompt Engin... by gremlinsinc on Tuesday April 11, 2023 @11:16AM (#35526345)

but see, that is how humans logic and reason. humans are given a set of specs, write code and the client often comes back and says that it's not what they wanted, because their spec sucked or wasn't specific enough.. this happens daily on development teams everywhere. Gpt4 literally is doing the same thing, trying to fulfill a spec, which is whatever we give it and sometimes we just confuse it.

I'd be rich if I had a dollar everytime a stakeholder confused me trying to explain their desired outcome. Especially when I was a junior, now I'm more careful about starting, before understanding but that's equivalent to my version of: sorry I'm a human developer, not a kind reader, i.e. as a large language model...

[ Parent | on HN ]