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 youā€™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 cutā€™nā€™paste it into its own post ā€” thereā€™s no quota for posting and the bar really isnā€™t that high.

The post Xitter web has spawned soo many ā€œesotericā€ right wing freaks, but thereā€™s no appropriate sneer-space for them. Iā€™m talking redscare-ish, reality challenged ā€œculture criticsā€ who write about everything but understand nothing. Iā€™m talking about reply-guys who make the same 6 tweets about the same 3 subjects. Theyā€™re inescapable at this point, yet I donā€™t see them mocked (as much as they should be)

Like, there was one dude a while back who insisted that women couldnā€™t be surgeons because they didnā€™t believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I canā€™t escape them, I would love to sneer at them.

    • corbin@awful.systems
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      4 months ago
      NSFW (including funny example, don't worry)

      RAG is ā€œRetrieval-Augmented Generationā€. Itā€™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, hereā€™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.

      > Batman, what's your favorite time of day?
      Batman thinks to themself: I am vengeance. I am the night.
      Batman thinks to themself: I strike from the shadows.
      Batman thinks to themself: I don't play favorites. I don't have preferences.
      Batman says: I like the night. The twilight. The shadows getting longer.
      
    • pyrex@awful.systems
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      4 months ago

      Itā€™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.

    • self@awful.systems
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      4 months ago

      so, uh, you remember AskJeeves?

      (alternative answer: the third buzzword in a row thatā€™s supposed to make LLMs good, after multimodal and multiagent systems absolutely failed to do anything of note)