We’ve had this in the engineering world a long time in the form of Google and StackOverflow searches.
The great engineers i’ve worked with know most stuff they need day-to-day, but when they run into issues it’s their ability to search for and find the right answer that gives them advantage over others who don’t know where to look (or even start).
Another nice thing with language models is that they condense the information, it doesn't matter how many webpages there are for a product, if you ask ChatGPT about it, it will list it exactly once. It knows it is the same product each time and the list it produces will be created specifically for your query. Meanwhile Google gets cluttered with duplicate information, since your query is implicitly about webpages mentioning a product, not products themselves.
That said, there is still plenty of work to do here. BingChat is completely unusable when it comes to product search, much worse than Google, as it will just pick the first three search results, which tend to be SEO spam, and summarize their content. ChatGPT is much better here, but its lack of direct Web access also handicaps it rather heavily.
Getting excellent, exact performance out of deterministic systems is an impressive feat, but autonomy means variability, and getting excellent performance out of variable systems (especially illegible ones) is a different game.
> Simply prompting a model to “think step by step” can lead it to perform well on entire categories of math and reasoning problems that it would otherwise fail on (Kojima et al., 2022). Similarly, even observing that an LLM consistently fails at some task is far from sufficient evidence that no other LLM can do that task (Bowman, 2022).
The eerie thing to me is that this is coaching. I have never coached anything that isn’t alive by any definition. My feelings about this realization are ambivalent.
I’ve never seen code look so correct, sort of work but be so strangely crap.
Complication meaning something slightly beyond changing variable names: you need a different if condition, the snippet doesn't contain the required import, the version has advanced slightly.
Google can do that too. (Well, it could if it wasn't permanently broken from SEO spam and monetization efforts.)
Which is the key takeaway: the "chat" part of ChatGPT is a stupid smoke and mirrors gimmick. The salient useful parts are the large and well-curated training database.
Which is technically quite possible to do without "AI" or text generators; though possibly not realistic from a business sense. There's no business case for "information search without spam and ads".
This is my experience of GPT generated code too. It needs refactoring as soon as it's generated for me to be happy with it.
The real question is whether that actually matters though. If an AI tool is writing ugly code that works, and the 'developer' is only interacting with that code through prompts, then what the code looks like is irrevelant. No one will see it. It'd be like worrying about what the inside of a Photoshop file or a Word doc looks like.
I think we're some way off using AI to write and modify code like it's an opaque file, but once we're there it won't matter if that code is ugly and terrible. No one will look.
I wonder if this skill can become more useful in the age of "prompt engineering"? Will my googling skills be useless and I need to start over? Or will my ability to google naturally convert into a decent prompt-engineering skill given a reasonable amount of practice?
there's two parts, good google query, but also figuring out which of the results returned is the most likely to be useful.
I've often found results for people that were on the first page of the most obvious query in the world to start with, but they were not the first result but down a bit and maybe looked a bit weird.
That's the basic — sometimes only — input to a search query. I suspect the ability to comprehend and describe a problem is a skill that translates quite smoothly to a world of prompt-based interaction.
In fact one trick is to go to LLM to get the "closed-book" answer, and then use that answer to formulate a proper search query for Google.
Q: What is the height of Everest.
A: The height of Everest is 8800m
Search: "The height of Everest is 8800m"
Result: 8849m
Using the generated answer as search phrase, even if it is factually wrong, works because it has the right structure and good keywords.