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How many proprietary use cases truly need pre-training or even fine-tuning as opposed to RAG approach? And at what point does it make sense to pre-train/fine tu
by ryeguy_24 6mo ago
How many proprietary use cases truly need pre-training or even fine-tuning as opposed to RAG approach? And at what point does it make sense to pre-train/fine tune? Curious.
- baby 6mo ago[flagged]
- charcircuit 6mo agoUsing tools and skills to retrieve data or files is anything but dead.
- nathanappere 6mo agoI think people just mean "using vector databases to enable RAG".
- menaerus 6mo agoEven that doesn't make sense. Why would you not build a vector database to complement your RAG engine?
- charcircuit 6mo agoFor coding use cases you may want a way to search for symbols themselves or do a plain text exact match for the name of a symbol to find the relevant documents to include. There is more to searching than building a basic similarity search.
- menaerus 6mo agoSorry but who mentioned coding as a use-case? My comment was general and not specific to the coding use-case, and I don't understand where did you get the idea from that I am arguing that building a similarity search engine would be a substitute to the symbol-search engine or that symbol-search is inferior to the similarity-search? Please don't put words into my mouth. My question was genuine without making any presumptions. Even with the coding use-case you would still likely want to build a similarity search engine because searching through plain symbols isn't enough to build a contextual understanding of higher-level concepts in the code.
- charcircuit 6mo agoI mentioned coding as a use case in my comment you replied to. You were asking for an example for when one wouldn't use vector search and I provided one. I did not say similarity search would be a substitute. I said that for the coding case you do not need it. >you would still likely want to build a similarity search engine In practice tools like Claude Code, Codex, Gemini, Kimi Code, etc are getting away with searching for code with grep / find and understanding code by loading a sufficient amount of code into the context window. It is sufficient to understand higher level concepts in the code. The extra complexity of maintaining vector database top of this is not free and requires extra complexity.
- menaerus 6mo agoIn your point you said "There is more to searching than building a basic similarity search." which assumed and implied all kinds of things and which was completely unnecessary. > In practice tools like Claude Code, Codex, Gemini, Kimi Code, etc are getting away with searching for code with grep / find and understanding code by loading a sufficient amount of code into the context window Getting away is the formulation I would use as well. "Sufficient amount" OTOH is arguable and subjective. What suffices in one usage example, it does not in another, so the perception of how sufficient it really is depends on the usage patterns, e.g. type and size of the codebases and actual queries asked. The crux of the problem is what amount and what parts of the codebase do you want to load into the context while not blowing up the context and while still maintaining the capability of the model to be able to reason about the codebase correctly. And I find it hard to argue that building the vector database would not help exactly in that problem.
- CharlesW 6mo ago[flagged]
- strongly-typed 6mo agoWait, what does NFTs have to do with RAG?
- elicash 6mo agoI have no interest in anything crypto, but they are making a proposal about NFTs tied to AI (LLMs and verifiable machine learning) so they can make ownership decisions. So it'd be alive in the making decisions sense, not in a "the technology is thriving" sense.
- nl 6mo agoI don't think RAG is dead, and I don't think NFTs have any use and think that they are completely dead. But the OP's blog is more about ZK than about NFTs, and crypto is the only place funding work on ZK. It's kind of a devil's bargain, but I've taken crypto money to work on privacy preserving tech before and would again.
- bigyabai 6mo agoIn what, X's hype circles? Embeddings are used in production constantly.
- loeg 6mo agoIs it??
- Shitty-kitty 6mo agorag basically gives the llm a bunch of documents to search thru for the answer. What it doesn't do is make the algorithm any better. pre-training and fine-tunning improve the llm abaility to reason about your task.
- mirekrusin 6mo agoYou can fine tune small, very fast and cheap to run specialized models ie. to react to logs, tool use and domain knowledge, possibly removing network llm comms altogether etc.
- troyvit 6mo agoI'm thinking stuff like this: https://denverite.com/2026/03/12/ai-recycling-facility-commerce-city/ https://denverite.com/2026/03/12/ai-recycling-facility-comme... You could take a model like the one referenced in the article, retool it with Forge for oh I don't know, compost, and use it to flag batches that contain too much paper for instance. These kinds of applications would work across industries, basically anywhere where you have a documented process and can stand to have automated oversight.