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 youāll 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 cutānāpaste it into its own post ā thereās no quota for posting and the bar really isnāt that high.
The post Xitter web has spawned soo many āesotericā right wing freaks, but thereās no appropriate sneer-space for them. Iām talking redscare-ish, reality challenged āculture criticsā who write about everything but understand nothing. Iām talking about reply-guys who make the same 6 tweets about the same 3 subjects. Theyāre inescapable at this point, yet I donāt see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldnāt be surgeons because they didnāt believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I canāt escape them, I would love to sneer at them.
Current flavor AI is certainly getting demystified a lot among enterprise people. Letās dip our toes into using an LLM to make our hoard of internal documents more accessible, itās supposed to actually be good at that, right? is slowly giving way to āWhat do you mean RAG is basically LLM flavored elasticsearch only more annoying and less documented? And why is all the tooling so bad?ā
Our BI team is trying to implement some RAG via Microsoft Fabrics and Azure AI search because we need that for whatever reason, and theyāve burned through almost 10k for the first half of the running month already, either because itās just super expensive or because itās so terribly documented that they canāt get it to work and have to try again and again. Normal costs are somewhere around 2k for the whole month for traffic + servers + database and I havenāt got the foggiest whatās even going on there.
But someone from the C suite apparently wrote them a blank check because itās AI ā¦
Confucius, the Buddha, and Lao Tzu gather around a newly-opened barrel of vinegar.
Confucius tastes the vinegar and perceives bitterness.
The Buddha tastes the vinegar and perceives sourness.
Lao Tzu tastes the vinegar and perceives sweetness, and he says, āFellas, I donāt know what this is but it sure as fuck isnāt vinegar. How much did you pay for it?ā
The fuckās a rag in an AI context
NSFW (including funny example, don't worry)
RAG is āRetrieval-Augmented Generationā. Itās 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, hereās 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.
Itās 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 thatās supposed to make LLMs good, after multimodal and multiagent systems absolutely failed to do anything of note)
I always saw it more as LMGTFYaaS.
Maybe hot take, but I actually feel like the world doesnāt need strictly speaking more documentation tooling at all, LLM / RAG or otherwise.
Companies probably actually need to curate down their documents so that simpler thinks work, then it doesnāt cost ever increasing infrastructure to overcome the problems that previous investment actually literally caused.
Definitely, but the current narrative is that you donāt need to do any of that, as long as you add three spoonfulls of AI into the mix youāll be as good as.
Then you find out what you actually signed up for is to do all the manual preparation of building an on-premise search engine to query unstructured data, and you still might end up with a tool thatās only slightly better than trying to grep a bunch of pdfs at the same time.