Earlier quoted context omitted.
I’m starting to think there’s an LLM equivalent to the old saying about how everything the media writes is accurate except on the topics you’re an expert in. All LLM output looks to be good quality except when it’s output you’re an expert in. People who have no background in writing or editing think LLMs will revolutionize those fields. Actual writers and editors take one look at LLM output and can see it’s basically…
LLMs are not worthless for programming. You just cannot expect it to ship a full programm for you, but for generating functions with limited scope, I found it very useful. How to make use of a new and common libary for example. But of course you have to check and test. And for text I know people who use it succesfully (professionally) to generate texts for them as a summary from some data. They still have to proof re…
They can be worse than worthless. They can sabotage your work if you let them making you spend even more time fixing it afterwards.
For an example. I've used Gpt4 as a sort of Google on steroids with prompts like "do subnets in gcloud span azs" and ", "in gcloud secret manager can you access secrets across regions". I very quickly learned to ask "is it true" after every answer and to never rely on a given answer too much(verify it quickly, don't let misinformation get you too far down the wrong route). So is it useful? Yes, but can it lead you down the wrong path? It very well can. The least experience you have in the field the easier it will happen.
>You just cannot expect it to ship a full programm for you, but for generating functions with limited scope, I found it very useful
Entire functions? Wow. I found it useful for generating skeletons I then have to fill by hand or tweak. I don't think I ever got anything out of Gpt4 that is useful as is (maybe except short snippets 3 lines long).
However, I found it extremely useful in parsing emails received from people or writing nice sounding replies. For that it is really good (in English).