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GPT-Prompt-Engineer

github.com

11–20 of 166 posts

Re: GPT-Prompt-Engineer

#14

Should we really call it "engineering" if its a case of "try random things until one of them works without really knowing why"?

That's called machine learning.

Gradient descent type optimization is far from "trying random things until one of them works without really knowing why". You can calculate all partial derivatives and understand the impact.

Re: GPT-Prompt-Engineer

#15

Should we really call it "engineering" if its a case of "try random things until one of them works without really knowing why"?

Looks more like science here. Run a bunch of experiments and see which does better.

But literally the first sentence of the readme is "Prompt engineering is kind of like alchemy."

Re: GPT-Prompt-Engineer

#17
post #15

Should we really call it "engineering" if its a case of "try random things until one of them works without really knowing why"?

Looks more like science here. Run a bunch of experiments and see which does better. But literally the first sentence of the readme is "Prompt engineering is kind of like alchemy."

alchemy is kind of like science before a lot of principles were formalized.

Re: GPT-Prompt-Engineer

#18
post #14

Earlier quoted context omitted.

That's called machine learning.

Gradient descent type optimization is far from "trying random things until one of them works without really knowing why". You can calculate all partial derivatives and understand the impact.

[deleted]

Re: GPT-Prompt-Engineer

#19
post #9
post #6

How are they actually ranked?

It seems like a `ranking_system_prompt` is used to rank the output of other prompts, which is pretty cool! > Your job is to rank the quality of two outputs generated by different prompts. The prompts are used to generate a response for a given task. You will be provided with the task description, the test prompt, and two generations - one for each system prompt. Rank the generations in order of quality. If Generation…

How did they rank the ranking prompt?

Re: GPT-Prompt-Engineer

#20
post #14

Earlier quoted context omitted.

That's called machine learning.

Gradient descent type optimization is far from "trying random things until one of them works without really knowing why". You can calculate all partial derivatives and understand the impact.

Sure you can, but you can also throw things in randomly and see what works then build theory around it.
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