this post was submitted on 17 Aug 2024
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Then just write that.
I don't understand why we're having AIs verboseify simple information?
Why do many word if few word do trick.
How long until we start using LLMs to summarize messages over-verbalized by LLMs?
And offloading the accounting for context WILL bite you in the ass. If you can't remember what a discussion was about and what needs considering, you're no longer doing the thinking.
Because in my experience some business clients feel offended or upset that you aren’t being formal with them. American businesses seem to care less I noticed but outside of the USA (particularly in Germany) I noticed that formality serves better. Also the LLM uses the thread history to add context. Stuff like “I know we agreed on meeting on Tuesday at last meeting but unfortunately I can’t do that…” this stuff matters to clients.
I don’t offload because I don’t remember. I offload because it saves me time. Of course I read what is written before I send it out.
Being formal and considerate does not require being that much more verbose.
Do you really save time running messages through an LLM vs just writing them as you think of what to say?
The LLM responses are more verbose but not a crazy amount so. It’s mostly adding polite social padding that some people appreciate.
As for time totally. It’s faster to write “can’t go to meeting, suggest rescheduling it for Thursday.” And proofread than to write a full boomer style letter.
I feel like we might write at very different WPMs. For me, proofreading and fixing AI slop takes longer than just writing things myself.
And another difference might be that to me and everyone I work with, writing in full on "boomer" is considered an insulting waste of everyone's time.
Which it is.
It’s a waste of everyone’s time for sure. It’s just good business sense to make your customers happy though.
As for typing speed perhaps ya lol. You could be faster. But I think the best approach here is using high quality locally run LLMs that don’t produce slop. For me I can count on one hand how many times I’ve had to correct things in the past month. It’s a mater of understanding how LLMs work and fine tuning. (Emphasis on the fine tuning)