Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

GPT-Prompt-Engineer

github.com

Post reply on HN
Print view View original

111–120 of 166 posts

Re: GPT-Prompt-Engineer

#111

Post by rmilejczz »

jes wrote: post #96
tough wrote: post #84

Earlier quoted context omitted.

> One day I think there will be true software engineering. When that happens you won't be able to start software projects without certifications, and most people (or programs!) who actually do the coding will be following careful plans and instructions from the engineers who designed the project. Sounds really bad. Software can't fall on your head and kill you, not all of it at least. Different software should requir…

You might enjoy this article on the Therac-25 [1]. It's kind of the standard example of how errors in software can wind up harming people. I have written medical device software for about 30 years. In my experience, delivering high quality software for Class B and Class C devices is both challenging and expensive. https://en.wikipedia.org/wiki/Therac-25

Every software developer should know this story, it is a humbling and important lesson. Yes luckily most of can’t ship code that accidentally kills people but we can absolutely empathize with the conditions which led to it happening.

I recommend reading the entire postmortem, http://sunnyday.mit.edu/papers/therac.pdf yes it is quite long but if you write code in any capacity it’s worth the read

Re: GPT-Prompt-Engineer

#112

Post by broast »

FishInTheWater wrote: post #49
brunoluiz wrote: post #34

Isn’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?

Prompt "engineering" is just writing prayers to forest faeries. Whilst BASIC/JavaScript/etc are all magic incantations to a child, a child will soon figure out there's underlaying logic, and learn the ability to reason about what code does, and what certain changes will do. With prompts, it's all faerie logic. There is nothing to learn, there are only magic incantations that change drastically if the model is updated…

I don't get where this sentiment comes from. I build software specifically on the concept of predictable results from llm's being composable.

Sure, the results are not deterministic in that 100% of the time the exact prompt returns the exact same result, but you can tune your prompts so that 100% of the time they give you a valid result in the result category you were seeking, and with a specific probability distribution of available choices.

Prompts are functions that can take concrete input and create a probabilistic output that can be automated upon. Especially if you only need to output one token, i.e a number, boolean, word, object reference. And for obvious reasons - the further you forecast out in a sequence the less accurate you will be.

As long as you don't change the underlying model, in a massive model with billions of parameters, there are definitely mechanisms and behaviors to discover that you can reason about.

Re: GPT-Prompt-Engineer

#113

Post by martincmartin »

donkeyboy wrote: post #67
fatso784 wrote: post #65

This 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.

correlation ... strongly positive.

A positive correlation just means better than chance. "Strongly" is vague, and might not be much better than chance.

Re: GPT-Prompt-Engineer

#114

Post by dragonwriter »

martincmartin wrote: post #113
donkeyboy wrote: post #67

Earlier quoted context omitted.

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.

correlation ... strongly positive. A positive correlation just means better than chance. "Strongly" is vague, and might not be much better than chance.

> A positive correlation just means better than chance. "Strongly" is vague, and might not be much better than chance.

No, adverbs like “strongly” modify adjectives (or verbs, but that’s not relevant here) not nouns; “strongly” is an intensifier that modifies “positive”, its not a separate adjective that modifies the noun “correlation”.

Re: GPT-Prompt-Engineer

#115

Post by damascus »

jstanley wrote: post #72
donkeyboy wrote: post #67

Earlier quoted context omitted.

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.

That's definitely begging the question. If you're prepared to accept that GPT-4 can answer questions just as well as humans can, why do you even need to do prompt engineering?

Humans still need 'prompt engineering' to answer questions more accurately though.

* What's the best way to get to Radio Shack from here?

is not the same as

* What's the easiest way to get to Radio Shack from memory when riding a bicycle from here?

Re: GPT-Prompt-Engineer

#116

Post by FishInTheWater »

broast wrote: post #112
FishInTheWater wrote: post #49

Earlier quoted context omitted.

Prompt "engineering" is just writing prayers to forest faeries. Whilst BASIC/JavaScript/etc are all magic incantations to a child, a child will soon figure out there's underlaying logic, and learn the ability to reason about what code does, and what certain changes will do. With prompts, it's all faerie logic. There is nothing to learn, there are only magic incantations that change drastically if the model is updated…

I don't get where this sentiment comes from. I build software specifically on the concept of predictable results from llm's being composable. Sure, the results are not deterministic in that 100% of the time the exact prompt returns the exact same result, but you can tune your prompts so that 100% of the time they give you a valid result in the result category you were seeking, and with a specific probability distribu…

but you can tune your prompts so that 100% of the time they give you a valid result in the result

You can't though, that's the issue. Illustrative here are tokens like "SolidGoldMagikarp", but this does happen to "normal" sequences of tokens as well.

There is no filter you can build to keep out such mistakes, any set of otherwise normal tokens could trigger the model to produce wrong output.

Because of how large these models and most prompts are, even slight changes in things like attention can cascade into extremely different results.

there are definitely mechanisms and behaviors to discover that you can reason about.

It's faerie logic. The behaviours are mere trends and observations, not underlaying truth.

The faeries reward you for offering them fruit. But offer them apple which fell from the tree exactly 74 hours ago down to the second and they'll kill you. There is no way to know ahead of time which things will upset them.

The risk here is that you're fooled into believing these systems are understandable, that you know how they work, and that you'll mistakenly use them for something where the wrong results have consequences. You'll stop double-checking the output, all humans are lazy like that, and then you'll have disaster on your hands.

Re: GPT-Prompt-Engineer

#117

Post by wil421 »

groestl wrote: post #61
pavel_lishin wrote: post #54

Earlier quoted context omitted.

Canada?

Austria

How does certification work? I’d imagine it would be in a less abstracted language like C or C++. The problem for me is most of my schooling was based on web technologies and 2 classes of Java.

I wound hate having to studying for a C based test when the area I work in is all web tech. Same could be said of Java. I learned it in school and haven’t used it in 10 years except for it being the backend on my first front end dev project.

Re: GPT-Prompt-Engineer

#118

Post by [deleted] »

fatso784 wrote: post #65

This 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…

[deleted]

Re: GPT-Prompt-Engineer

#119

Post by oefnak »

albrewer wrote: post #110
flangola7 wrote: post #102

Earlier quoted context omitted.

I just asked and it told me cows are mammals and do not lay eggs. That Reddit post is not even GPT-4 and is 5 months old, which may as well be the 19th century on AI tech timescales.

The post you're replying to is a case in point. This time it's cow eggs; what next?

Some people believe the earth is flat, but they can still provide useful work.

Re: GPT-Prompt-Engineer

#120

Post by typpo »

fatso784 wrote: post #65

This 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…

Thanks for mentioning promptfoo. For anyone else who might prefer deterministic, programmatic evaluation of LLM outputs, I've been building this for evaluating prompts and models: https://github.com/typpo/promptfoo

Example asserts include basic string checks, regex, is-json, cosine similarity, etc. (and LLM self-eval is an option if you'd like).

Post reply on HN