Earlier quoted context omitted.
I am a Software Engineer, I am certified (since in my country, they do so, just as with Mechanical Engineers). Still think it's overloaded though.
Canada?
Austria
GPT-Prompt-Engineer
61–70 of 166 posts
Re: GPT-Prompt-Engineer
#62People don't understand intelligence
Re: GPT-Prompt-Engineer
#63Isn’t engineering an exact science while prompt engineering is completely not? Although, even software engineering being an exact science, it is a funny one: most of us don’t get certified as like, let’s say, mechanical engineers do. Would they say we are engineers? So perhaps the “engineer” term got overloaded in recent years?
I am a Software Engineer, I am certified (since in my country, they do so, just as with Mechanical Engineers). Still think it's overloaded though.
People who have not studied and been given that distinction in engineering going on to call themselves engineers is the reason the term is overloaded.
I've always assumed there's a near 100% overlap between people using the term wrongly to describe any programming activity, and people complaining that it has no meaning or is self-aggrandisement
Re: GPT-Prompt-Engineer
#64Earlier quoted context omitted.
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.
I think it is a freaking miracle it even works. I understand how it works for say 100 parameter linear regression, but that it would work for billions of parameters (billion dimensional space) by nudging the parameters each a little (based on purely it's impact on the loss, assuming everything stays the same), is not obvious to me. It is a kind of magic. Regarding randomness, the initialization of the weights is rand…
To be honest, there’s always been a tension in machine learning between the: “we won’t do it unless the theory is complete and sound” versus the “we don’t understand this but it works much better consistently so we do it” crowd.
In the 90s and even 00s, the theory first crowd was mainstream and the empirical first crowd was considered fringe. Very fringe.
Personally I appreciated it when LeCun was like: “you can’t find the solution if you only search where the lamplight is shining.” Or other early deep learning practitioners note that ML theory is usually so far disconnected from practice in terms of tightness and bounds that you might as well ignore pure theory completely.
Anyway, it wasn’t until deep learning methods really smashed benchmarks across the board did people give in to the black magic / alchemy driven approaches of empiricism based upon intuition and bias developed through long-held experience.
Re: GPT-Prompt-Engineer
#65This tool doesn’t benchmark based on how a model actually responds to the generated prompts. Instead, it trusts GPT4 to rank prompts simply in terms of how well it imagines they will perform head-to-head. Thus, there’s no way to tell if the chosen ‘best prompt’ actually is the best, because there’s no ground truth against actual responses.
Why is this so popular, then (more popular than promptfoo, which I think is a much better tool in the same vein)? AI devs seem enamored with the idea of LLMs evaluating LLMs —everything is ‘auto-‘ this and that. They’re in for a rude awakening. The truth is, there are no shortcuts to evaluating performance in real world applications.
Re: GPT-Prompt-Engineer
#66Earlier quoted context omitted.
His genius laid in not only seeing which technologies were coming, but predicting what perversions business people would twist it into.
Creating a computer to find "the ultimate answer to life, the universe and everything", getting a cryptic answer and then creating an even bigger and more complicated computer to find the question is a pretty good satire of generative ai based chatbots of exponentially increasing model size.
Even down to the blind faith that there was an accurate calculation performed, which ultimately justified the planet-computer.
Re: GPT-Prompt-Engineer
#67This tool doesn’t benchmark based on how a model actually responds to the generated prompts. Instead, it trusts GPT4 to rank prompts simply in terms of how well it imagines they will perform head-to-head. Thus, there’s no way to tell if the chosen ‘best prompt’ actually is the best, because there’s no ground truth against actual responses. Why is this so popular, then (more popular than promptfoo, which I think is a…
There is a paper on arxiv saying that GPT4 correlation with human evaluators on a variety of tasks with strongly positive. I am also uncomfortable with it, but using GPT4 as a grader is not as bad as you think.
Re: GPT-Prompt-Engineer
#68How 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…
A neat feature would be able to switch between autoranking and manual ranking.
Re: GPT-Prompt-Engineer
#69This tool doesn’t benchmark based on how a model actually responds to the generated prompts. Instead, it trusts GPT4 to rank prompts simply in terms of how well it imagines they will perform head-to-head. Thus, there’s no way to tell if the chosen ‘best prompt’ actually is the best, because there’s no ground truth against actual responses. Why is this so popular, then (more popular than promptfoo, which I think is a…
There is a paper on arxiv saying that GPT4 correlation with human evaluators on a variety of tasks with strongly positive. I am also uncomfortable with it, but using GPT4 as a grader is not as bad as you think.
You’re missing the point here. It’s not even getting the LLM’s opinion on evaluating the responses to the prompts (which itself is fraught for some tasks, and benchmarks are known to be limited —even OpenAI admits this, it’s why they made evals). It’s one level abstracted from that. It’s evaluating what the LLM thinks of how well the prompt will do, in purely hypothetical terms. That’s hogwash —different LLMs perform very differently even for the same prompts. Try any tool that lets you compare model responses side-by-side. Unless I see actual use cases, this is yet another iteration of overtrusting AI.
Here is what HN was talking about, nearly three months ago -the exact same type of ‘auto-prompt-gen’ tool: https://news.ycombinator.com/item?id=35660751
Re: GPT-Prompt-Engineer
#70Isn’t engineering an exact science while prompt engineering is completely not? Although, even software engineering being an exact science, it is a funny one: most of us don’t get certified as like, let’s say, mechanical engineers do. Would they say we are engineers? So perhaps the “engineer” term got overloaded in recent years?
I am a Software Engineer, I am certified (since in my country, they do so, just as with Mechanical Engineers). Still think it's overloaded though.
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