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Comment by sigmoid10 | Hacker News Reader

I regularly use it for menial tasks and it has almost completely replaced searching documentations of major libraries for me. As a senior developer, it's a productivity gold mine if you learn how to use it correctly. But I wouldn't recommend it to any junior dev, because you need lots of experience to see when it starts hallucinating.

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> I regularly use it for menial tasks

Like random formal letter to AI? Maybe it is a societal problem more than a technical one if we all hate writing and reading these.

I don’t get the search documentation part. It has obvious blind spots on many things and hallucinate on others.

>Like random formal letter to AI?

No, I'm talking about things like writing SQL queries (even complex ones), CMake files, Docker configs or plotting stuff in Python. Of course, if you're not already an expert in these things, you'll have a hard time distinguishing useful replies from hallucinations - that's why I said it's mostly for senior devs. Without expert knowledge, you will likely not be able to benefit from it in its current state. But if you have that and know how to write efficient queries, it can easily up your productivity by a factor of 10 (i.e. going back and forth for 6 minutes with GPT4 to make it get your requirements can save you an hour of work looking through documentations).

10x seems like a stretch, but I agree. I have a chatgpt window open most of my workday now too. "Hey chatgpt - help me write this mathematical equation in latex. Hey chatgpt - give me an example nodejs program that reads a line-delimited JSON file without loading the whole thing in memory first". "Hey chatgpt - What are the popular & good python libraries for drawing an SVG scatter plot of some data? Give me some example code for each library."

It depends on what you're doing of course. With your example queries I'd also expect a lower boost, but the benefits should still be there. Where it really shines is if you already know exactly what you want and how you want it and you merely need someone to write it in code in a common language.

How do you reconcile with the fact that it imagine things?

GPT-4's solutions are usually correct the first time around, or can be corrected by telling it what it did wrong. The ask-clarify-correct loop is still faster and less effort than doing it yourself.

If people continue to learn outside of LLMs and can tell when it is wrong so they can correct it. The slope can be slippery. For those of us who grew up before the rise of LLMs, this isn't really something we have to worry about. But the next generation will be not know what it was like without LLMs, kind of like the pre/post-internet/phone generational splits

The same way a colleague or myself is often not 100% correct. Neither is Google.

Not a problem in practice if you're a developer. It's probably worse if you're just copypasting without understanding anything.

Approach it with the same critical eye that you use to consume any other information.

I've found that for questions that aren't super complex on things that are extremely likely to be in its training dataset (such as public documentation of popular Python libraries) the error rate is very close to 0%. Even compared to GPT from a few months ago I've found the accuracy of responses has increased dramatically.

I still believe anyone using these tools on a day-to-day basis should have a sense of "trust but verify."

As a senior developer: Experience. The output from ChatGPT isn't perfect, but it's good enough to point one in the right direction. The code it outputs is mostly garbage that a good developer would never put into production, but what it does fairly well is finding the relevant bits of documentation.

Example: Using a small part of a new, big, unfamiliar library. Rather than digging through the library docs, I can ask ChatGPT about it, which often points me to the relevant parts, which I then can still confirm in the official docs.

Yeah. I got chatgpt to write some code using Indexeddb the other day. The code was absolute rubbish, and I rewrote it to be half as long, while being clearer and with better error handling.

But the code it gave me was a great starting point. I found it much faster & easier to rewrite the bad code it wrote than pore through documentation and figure out how to solve my problem from scratch.

I more-or-less know what I expect it to give me, so I can just go somewhere else then. I tend to ask it three broad sets of queries, since it's what it does best at. tl;dr: it can transform text very well, even with lots of ambiguity. also it knows technical vocab

- if I'm working in a field that's new to me or that I don't understand, I ask for help understanding the basics and vocabulary of the field. it does very well at this.

- if I have a well defined problem, but am simply not familiar with the libraries for a given situation, it tends to do a good job translating my english queries into the right code. I do examine and test the code afterwards to make sure it's correct. You can really see this in action when you ask it to do data analysis; and the REPL loop in that mode is also great at catching bugs.

- if I have like a copy-paste from a documentation site, I can ask it to transform that into code or into a better-formatted version. this saves a lot of time, and I don't have to remember regexes or vim keybinds

> if I'm working in a field that's new to me or that I don't understand, I ask for help understanding the basics and vocabulary of the field. it does very well at this

I also do this, but I am careful about being confident that "it does very well at this". We can't actually evaluate what it's putting out, other than that it sounds plausible, which is something LLMs are truly great at.

> As a senior developer it's a productivity gold mine if you learn how to use it correctly.

Sounds like what a junior developer would say, given they tend to depend on it even when it hallucinates the wrong answers badly.

Also explains the rampant title inflation that is going around in the tech industry these days.

Would have to disagree. I've writing code for +25 years and counting, a good portion is critical infraestructure code. I've reached the title of chief-expert and quite frankly gets difficult nowdays to find people who can answer my questions or at least help explore possible routes to solve them. ChatGPT is like a buddy that is always available and sitting next to you for a good technical chat.

Sometimes it comes up with really good techniques that are different from my usual approaches, other times is plain wrong and I'm correcting it.

I am more productive with the ChatGPT in my life. Whenever stuck on some weird error, instead of googling I paste the log and discuss with the bot what can be going wrong there. We can talk about pointer analysis, performance bottleneck comparisons. Could I do it all on my own? Sure. However it is boring and quite certainly would require 10x more time to perform all calculations on my own.

In the end of a day it is just another tool. Brings advantage when used properly.

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