The main use case for LLMs is writing text nobody wanted to read. The other use case is summarizing text nobody wanted to read. Except they don’t do that either. The Australian Securities and…
I had GPT 3.5 break down 6x 45-minute verbatim interviews into bulleted summaries and it did great. I even asked it to anonymize people’s names and it did that too. I did re-read the summaries to make sure no duplicate info or hallucinations existed and it only needed a couple of corrections.
good tools are designed well enough so it’s clear how they are used, held, or what-fucking-ever.
fuck these simpleton takes are a pain in the arse. They’re always pushed by these idiots that have based their whole world view on fortune cookie aphorisms
How did you make sure no hallucinations existed without reading the source material; and if you read the source material, what did using an LLM save you?
I also use it for that pretty often. I always double check and usually it’s pretty good. Once in a great while it turns the summary into a complete shitshow but I always catch it on a reread, ask a second time, and it fixes things up. My biggest problem is that I’m dragged into too many useless meetings every week and this saves a ton of time over rereading entire transcripts and doing a poor job of summarizing because I have real work to get back to.
I also use it as a rubber duck. It works pretty well if you tell it what it’s doing and tell it to ask questions.
what if your rubber duck released just an entire fuckton of CO2 into the environment constantly, even when you weren’t talking to it? surely that means it’s better
I had GPT 3.5 break down 6x 45-minute verbatim interviews into bulleted summaries and it did great. I even asked it to anonymize people’s names and it did that too. I did re-read the summaries to make sure no duplicate info or hallucinations existed and it only needed a couple of corrections.
Beats manually summarizing that info myself.
Maybe their prompt sucks?
“Are you sure you’re holding it correctly?”
christ, every damn time
That is how tools tend to work, yes.
we find they tend to post here, though not for long
it makes me feel fucking ancient to find that this dipshit didn’t seem to get the remark, and it wasn’t even that long ago
Jobs is Tech Jesus, but Antennagate is only recorded in one of the apocryphal books
“tools” doesn’t mean “good”
good tools are designed well enough so it’s clear how they are used, held, or what-fucking-ever.
fuck these simpleton takes are a pain in the arse. They’re always pushed by these idiots that have based their whole world view on fortune cookie aphorisms
Said like a person who wouldn’t be able to correctly hold a hammer on first try
I got AcausalRobotGPT to summarise your post and it said “I’m not saying it’s always programming.dev, but”
@RagnarokOnline @dgerard “They failed to say the magic spells correctly”
Did you conduct or read all the interviews in full in order to verify no hallucinations?
How did you make sure no hallucinations existed without reading the source material; and if you read the source material, what did using an LLM save you?
I conducted the interviews myself alongside a colleague. The summary was for reporting our findings up to leadership.
I also use it for that pretty often. I always double check and usually it’s pretty good. Once in a great while it turns the summary into a complete shitshow but I always catch it on a reread, ask a second time, and it fixes things up. My biggest problem is that I’m dragged into too many useless meetings every week and this saves a ton of time over rereading entire transcripts and doing a poor job of summarizing because I have real work to get back to.
I also use it as a rubber duck. It works pretty well if you tell it what it’s doing and tell it to ask questions.
Isn’t the whole point of rubber duck debugging that the method works when talking to a literal rubber duck?
what if your rubber duck released just an entire fuckton of CO2 into the environment constantly, even when you weren’t talking to it? surely that means it’s better
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