I'm sorry, but then that article fails. It is very repetitive without clearly answering the differences. It gives the LLM feeling because it does not give a straight-forward answer to what the alternative actually is. Yes, something the agent can call itself while doing the task without having to pre-process the data, but that encompasses basically everything. What exactly is the proposal, what is the thing that was (according to the title) built?
Instead you get a quick answer box after the introduction that repeats the introduction, and the FAQ at the bottom leads with two similar sounding points. Even for an LLM-generated document that's not a good result.
I scanned through paragraph after paragraph of overbloated and repetitive descriptions of what and how RAG works compared to how "modern agents" need to access documents.
What I never saw at any point was whatever this "alternative to RAG" was supposed to be.
Honestly the entire body of copy feels like it was llm generated.
For example this one says 100% AI: https://www.salahadawi.com/hacker-news-ai-detector/49993820
A) searching using BM25 and via vector embeddings are two different methods of search; both have their uses, many practical use cases benefit from having both. The search method is independent of who calls it when, i.e. whether you just have a static pipeline, or a dynamic one where the search tool calls them.
B) you're describing the static pipeline as if it were SOTA. We dropped that well over a year ago IIRC. There might still be cases where that is good enough, but in general, agentic search has pretty much become the default since LLMs became reasonably good at tool calling.
ericol•45m ago
gael_dev•34m ago