This is why "prompt engineering" is going to be a legit job, still requiring the knowledge and sensibilities of an engineer/programmer. A jack of all trades problem solving gig. Maybe even constaultion businesses will build these glued together tools for bigger companies.
Sam Altman states that "prompt engineering", in his mind, is a result of a temporary limitations of the LLMs.
That he doesn't expect people to be doing what we call "prompt engineering" in 5 years. You'll simply ask for what you want and if there is ambiguity it can be sorted out interactively or it will just do the 'right' or obvious thing that we would expect.
I disagree with him. I've spent several years answering people's random questions (librarian) in multiple settings and my first W2 job back in 2004 was technological education. I also have a Linguistics degree and can program so I understand what LLMs can and can't do fairly well even if I'm nowhere near an expert. He's vastly underestimating the average person's awareness of their own thought process or even ability to accurately answer iterative questions in order to sort things out interactively. The weakness with such a claim isn't the AI, it's the humans' ability to provide the AI with accurate and useful information.
Not to mention that there's still a large portion of the general public that just freezes up when it comes to anything technological. I've provided support to a number of different communities and even educated people can't do this type of thinking easily.
It's possible that we could eventually get to that point, but it would require some massive educational efforts and a culture shift, which would also require substantial investment without a clear road to profit, and I believe America lacks the political will or even ability to implement anything like this. Since we can't act in unison, it will be down to companies, and for them it makes more sense to solve the problem by hiring prompt engineers with the savings they get from cutting elsewhere instead of training all their existing teams on how to learn new ways of thinking.
tl;dr: Technologically he's probably right, but he's massively overestimating the public's ability to use interactive tools to get information.
> obvious thing that we would expect
Any time you rely on something being 'obvious', the public is going to somehow fuck it up.
This is similar to the situation with software engineers interacting with other parts of the business or customers. It's IME the biggest skill of software engineers, not to write code, but to understand and translate business requirement in to code, while accounting for _existing_ (prior) requirements that were often forgotten. The code as such is usually straight forward, but the requirements on either end is where the work is.
This is also what programmers typically complain about when they say their PM isn't "technical" enough. What they usually mean is they don't understand the details of the business / domain well enough to meaningfully contribute to the organization / break up of the work.
To that end, I'd expect these language models to eventually improve at that task too, perhaps responding with choices or diagrams to help users learn how to ask what they actually want. That's in a sense what a prompt engineer is doing in this context. In that sense I think I agree w/ your assessment although I also think there's a lot of room for these AI's to handle that task, at least partially, as they improve.
I agree. I'm not a SWE because I don't want to code for 40+ hours a week, but I've been picking up projects here and there for small businesses for a long time (I started helping my dad do the same when I was 5-6) and so I manage projects end to end and so much of it is the back and forth with the clients. Knowing the right questions to ask and how people think about things are some of my most important skills, and in that respect my education and library experience has been very helpful. I'm far from the best programmer - hell, I'd consider myself below average but have enough experience to still be passable, but I'm easy to work with and that counts for a lot.
The issue with language models in particular is that to use them effectively you have to fundamentally understand that you're translating human-brain-language into computer-language and understand that those are in fact two different things even if their delivery happens identically. For most coders, this jump happens semi-instinctively because they know how to test something to verify 'ah yes, this follows computer logic'. The general public thinks computers are wizardry. The other group of people who will be able to understand this are those who have thought deeply about language for whatever reason and therefore know that human-brain-language has qualities that extend beyond 'We use words'. This is also very hard to impart to novices; I have a Linguistics degree and studied a cumulative 10 years of 6 languages so I went through that knowledge transfer as well. They're both hard and one or the other is necessary to interface with language models effectively and will be for likely the next 10ish years just because providing a good technical solution will require the technical people to understand much more about the social side of communication and that will also take time.
I thought about this for a while and I think I would boil it down to being used to dealing with language as data instead of just as a communication medium. Experience with corpora, sentiment analysis, and the various parts of linguistics does give you a solid grounding in the why the frequency distribution in the training set(s) occurs the way it does.
An example of things I consider when interfacing with an LLM that derive from my linguistic knowledge:
* That the language it's trained on is thoroughly modern/confined to the speech of a particular place in time. Which means that I know any viewpoint I receive is not only modern but from a very specific time and place. The language that everybody uses is sort of like the water fish swim in in that most people don't think about it. Which means I know that if I ask it something about (to use an example that is a culture war issue) the history of racism, I know that the answer is being run through modern conceptions of racism and if I want historical views, I need to get those elsewhere.
* That which words are most commonly used relies on the social and economic status of the speaker as well as word properties like phonetics and phonology. This makes it much easier to pick and choose which vocabulary and sentence structures to use in order to 'sub-pick' the part of the training set you want answers to. Asking 'how to grow peppers' and 'what soil variables control how well a Capsicum annuum plant grows' are going to get you different answers.
* Related to this, the differences between spoken and written English on a large scale - one problem with the 'everybody can just use LLMs' idea is that the LLMs are trained on written English but the majority of people interfacing with them speak to them as though it were a verbal conversation. There are parts of verbal communication that don't exist in written communication, and knowing how to provide scaffolding to make a request make sense requires understanding the difference between the two.
* A basic knowledge of sociolinguistics is fantastically helpful for developing personae and/or picking out biases in the training data set. (Somewhat similar to how I can usually ID an American's media diet and political affiliation after a 5-10 minute casual conversation).
> Technologically he's probably right, but he's massively overestimating the public's ability to use interactive tools to get information.
I think you are underestimating the pace of progress in the capability of the LLMs. It has infinite patience to query you for missing facts. Because it is not about the public's ability to use an interactive tool. It is about the LLM's capability to be a good focus group moderator/librarian/etc.
This role reversal is imminent from what it seems. The LLMs will be using us, rather than us using them.
This is exactly the things LLMs seem to be the worst at, even as they improve
LLMs are confidently wrong rather than inquisitive/have an interest in being right. They theoretically have infinite patience, but they really have no patience and accept input at face value.
I don't doubt that these can be improved, but I also don't doubt that some people will be better at interacting with LLMs than others, and that to get good at using an LLM will take onboarding time that organizations would prefer to centralize
This seems to resonate with me. I've got a bit of a tendency to experience spells of obsessiveness and if you get very pedantic and very wordy with GPT it espouses anything you tell it to that it conceptually could. Understanding human language to the max will be a useful skill because as with any agents communication is key to successful understanding
If it's a language model, yes. I think if it's in the initial prompt (first message) or second message to the AI doesn't make much difference for its role in the process.
