[roel_v]

I've watched (non technical) people use ChatGPT a few times now, and most of them have rather underwhelming results. The reason is that they think it's just some other search engine, and they phrase their prompts as 'search queries'. Or they go completely the other direction and think they can just throw in a few random words that somewhat describe what they're roughly thinking of, and then expect the computer to fill in the gaps.

It's 2023 and there are lots of people who don't know how to efficiently and effectively use Google. To be able to do that, you need some sort of mental model of crawlers and websites and what gets indexed and what not and at what frequency, and the results of SEO and how a somewhat savvy marketeer at some company might influence things etc. The same with LLM models - if you don't know what a 'token' is, your only chance of getting good results is to use these models a lot and then hope that you start building useful intuitions. It really doesn't come natural to most people like it does to most of us here.

[hnfong]

Do you have examples of people failing to get a good response from chatgpt because of bad prompts? I’m asking because at least for simple cases I can often just give it a very terse request and usually it will attempt to guess what I mean and give a reasonable answer. If not I can fix it with a follow up question.

My intuition is that language models have read terabytes of random internet data, and while presumably most developers of LLMs try to find high quality data, the models generally do ingest quite a bit of random stuff, and they try to make sense of those too, so in terms of understanding they are probably better than the strictness in format that we programmers are used to.

Of course the token thing is probably significant, but my understanding is that it affects the result only when you misspell your words(?)