LLMs are only 3-5 years old (NLP is much older OFC), for all we know they'll be a dead end in research like LSTM are today - LLM/Multi Modal just look super hot. "Attention is all you need" was released in 2017, it took 5 years to prove it was useful, for all we know the new hot thing has already been published and LLMs are obsolete - Google might have been right to wait.
Besides I dont think the top people at Google's DeepMind - and I can only "infer" this from watching them speek online - actually think LLM's are "the one".
Its cloudflare trying to enshittify the internet with micro transactions[0] and take their N% cut (of course it will start at like 2% but ask any uber driver how thats going).
The problem is the arguments they make for why this should happen are quite compelling, especially to those running sites (you'll see plenty of complaints on this forum about it), but theres also a large group of people who think information / code / data should be "free" (see open source code/maps/anything you can think off). So really its just a moral debate that will be lost in the interest of profit (which is ya know good n bad, if AI companies did more caching we probably wouldn't need this, but here we are).
I was going to argue "LLM's need code samples to-do well on languages and if we are honest C# is a language mostly held in private repo's" but Github's 2024 report[0] says its the 5th most used language (I'm to lazy to check if this report includes private repo's but I'll assume it doesn't).
I'm no ruby expert, so forgive my ignorance, but it looks like a small "NER model" packaged as a string convince wrapper named `filter` that tries to filter out "sensitive info" on input strings.
I assume the NER model is small enough to run on CPU at less than 1s~ per pass at the trade off of storage per instance (1s is fast enough in dev, in prod with long convos - that's a lot of inference time), generally a neat idea though.
Couple questions;
- NER doesn't often perform well in different domains, how accurate is the model?
- How do you actually allocate compute/storage for inferring on the NER model?
- Are you batching these `filter` calls or is it just sequential 1 by 1 calls
Yes and no, for human search - its kinda neat, you might find some duplicates, or some nearby neighbour bugs that help you solve a whole class of issues.
But the cool kids? They'd do something worse;
They'd define some complicated agentic setup that cloned your code base into containers firewalled off from the world, give prompts like;
Your expert software dev in MY_FAVE_LANG, here's a bug description 'LONG BUG DESCRIPTION' explore the code and write a solution. Here's some tools (read_file, write_file, ETC)
You'd then spawn as many of these as you can, per task, and have them all generate pull requests for the tasks. Review them with an LLM, then manually and accept PR's you wanted. Now your in the ultra money.
You'd use RAG to guide an untuned LLM on your code base for styles and how to write code. You'd write docs like "how to write an API, how to write a DB migration, ETC" and give that as tool to the agents writing the code.
With time and effort, you can write agents to be specific to your code base through fine tuning, but who's got that kind of money?
RAG is taking a bunch of docs, chunking them it to text blocks of a certain length (how best todo this up for debate), creating a search API that takes query (like a google search) and compares it to the document chunks (very much how your describing). Take the returned chunks, ignore the score from vector search, feed those chunks into a re-ranker with the original query (this step is important vector search mostly sucks), filter those re-ranked for the top 1/2 results and then format a prompt like;
The user ask 'long query', we fetched some docs (see below), answer the query based on the docs (reference the docs if u feel like it)
Doc1.pdf - Chunk N
Eat cheese
Doc2.pdf- Chunk Y
Dont eat cheese
You then expose the search API as a "tool" for the LLM to call, slightly reformatting the prompt above into a multi turn convo, and suddenly you're in ze money.
But once your users are happy with those results they'll want something dumb like the latest football scores, then you need a web tool - and then it never ends.
To be fair though, its pretty powerful once you've got in place.
Besides I dont think the top people at Google's DeepMind - and I can only "infer" this from watching them speek online - actually think LLM's are "the one".