Need to let loose a primal scream without collecting footnotes first? Have a sneer percolating in your system but not enough time/energy to make a whole post about it? Go forth and be mid: Welcome to the Stubsack, your first port of call for learning fresh Awful you’ll near-instantly regret.
Any awful.systems sub may be subsneered in this subthread, techtakes or no.
If your sneer seems higher quality than you thought, feel free to cut’n’paste it into its own post — there’s no quota for posting and the bar really isn’t that high.
The post Xitter web has spawned soo many “esoteric” right wing freaks, but there’s no appropriate sneer-space for them. I’m talking redscare-ish, reality challenged “culture critics” who write about everything but understand nothing. I’m talking about reply-guys who make the same 6 tweets about the same 3 subjects. They’re inescapable at this point, yet I don’t see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldn’t be surgeons because they didn’t believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I can’t escape them, I would love to sneer at them.
The fuck’s a rag in an AI context
It’s the technique of running a primary search against some other system, then feeding an LLM the top ~25 or so documents and asking it for the specific answer.
So you run a normal query but then run the results through an enshittifier to make sure nothing useful is actually returned to the user.
Basically
so, uh, you remember AskJeeves?
(alternative answer: the third buzzword in a row that’s supposed to make LLMs good, after multimodal and multiagent systems absolutely failed to do anything of note)
NSFW (including funny example, don't worry)
RAG is “Retrieval-Augmented Generation”. It’s a prompt-engineering technique where we run the prompt through a database query before giving it to the model as context. The results of the query are also included in the context.
In a certain simple and obvious sense, RAG has been part of search for a very long time, and the current innovation is merely using it alongside a hard prompt to a model.
My favorite example of RAG is Generative Agents. The idea is that the RAG query is sent to a database containing personalities, appointments, tasks, hopes, desires, etc. Concretely, here’s a synthetic trace of a RAG chat with Batman, who I like using as a test character because he is relatively two-dimensional. We ask a question, our RAG harness adds three relevant lines from a personality database, and the model generates a response.