[rsynnott]

> Ask it questions about accounting, other domains it knows

Be very careful here if you're using it for anything important! LLMs are quite good at answering questions about accounting in ways which are superficially convincing-looking, yet also complete nonsense. "But the magic robot told me it was okay" will not fly in a tax audit, say.

[graemep]

Exactly my immediate reaction. Accounting has to follow very strict rules and needs some application of judgement.

It might answer questions in a useful way, but you have to make sure you understand the answers and that they match accounting standards or tax rules (and one danger, at least in some places, is that they are different and you might apply the wrong one).

[notsydonia]

Unfortunately everything I've asked any of the main LLMs about where I actually legit knew the precise answer, they were wrong or half-right but excluding important context.

[cyberpunk]

I couldn’t be arsed typing a reference number into my online banking for a bill payment the other and it was a copy protected pdf, so I fired a screenshot into Claude and GPT and asked it to extract the details I need and both of them repeatedly got the OCR wrong.

I don’t trust these at all for anything apart from code which I can at least read/rewrite.

It’s quite nice for unit tests I guess. And weird k8s manifests you only write now again like batch/v1 CronJob or whatever.

I’m not panicking about my job just yet..

[leoedin]

I needed to normalise a big list of dates recently. I thought maybe GPT could help. It spat out a list of normalised dates which, after a bit of careful reading, were about 95% right.

How can you trust a tool that's right 95% of the time? In the end I wrote a script which handled edge cases explicitly. That took a little bit longer, but the output is deterministic. It took less time than manually cross referencing the output and input would have.

I tried asking GPT to write the conversion script instead, but the script it generated just didn't deal with the edge cases. After a few rounds of increasingly specific directions which didn't seem to be helping, I gave up.

I've been using copilot for development work. It has some magic moments, and it can be great for boilerplate. But then it introduces subtle bugs which are really hard to catch in review, or suggests completely incorrect function signatures and I wonder if it's adding very much at all.

The biggest problem with these tools is that they turn a fun problem solving exercise into an incredibly tedious reviewing exercise. I'd much rather do it myself and understand it fully than have to review the unreliable output of an LLM. I find it much simpler to be correct than to find flaws in other peoples work.

Am I missing something?

[rsynnott]

Erk. I’d actually kind of assumed that the likes of ChatGPT would offload OCR to, well, conventional OCR, which is, basically, a solved problem (possibly the only ‘AI’ thing which can be considered so).

[mukunda_johnson]

Agree, some domains are a big gray area. I used it to understand some accounting jargon while diagnosing a software issue, but anything that is not very commonplace is littered with misinformation, so you have to doublecheck. I find it more useful to gain a direction or lead on a topic rather than answers directly.

Of course many executives don't deal with the obscure stuff day to day (e.g. regular stuff people actually get paid to deal with) and think that LLMs can turn anyone into a superhero overnight :) The amount of times we were told that we were "putting our heads in the sand" regarding the advantages of AI was very annoying.