33
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
this post was submitted on 14 Jul 2024
33 points (100.0% liked)
TechTakes
1427 readers
107 users here now
Big brain tech dude got yet another clueless take over at HackerNews etc? Here's the place to vent. Orange site, VC foolishness, all welcome.
This is not debate club. Unless it’s amusing debate.
For actually-good tech, you want our NotAwfulTech community
founded 1 year ago
MODERATORS
The fuck's a rag in an AI context
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.
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)