This is Hamel, one of the authors of the article. We published the article with OReilly here:

Part 1: https://www.oreilly.com/radar/what-we-learned-from-a-year-of... Part 2: https://www.oreilly.com/radar/what-we-learned-from-a-year-of...

We were working on this webpage to collect the entire three part article in one place (the third part isn't published yet). We didn't expect anyone to notice the site! Either way, part 3 should be out in a week or so.

The entire field of information retrieval is still here. This was touched on by the OReilly article on lessons learned working with LLMS that hit the HN front page yesterday [1], in their section on RAG.

My sense is that you can currently break the whole thing down into two groups: the proverbial grownups in the room are typically building pipelines that are still doing it basically how the top-performing systems did in the '90s, with a souped up keyword and metadata search engine for the initial pass and an embedding model for catching some stuff it misses and/or result ranking. This isn't how most general-purpose search engines work, but it's likely how the ones you don't particularly mind using work. Web search, for example.

And then there's the proverbial internet comments section, which wants to skip past all the boring labor-intensive oldschool stuff, and instead just begin and end with approximate nearest neighbors search using an off-the-shelf embedding model. The primary advantage to this approach - and I should admit here that I've tried it myself - is that you can bodge it together over a weekend and have the blog post up by Monday.

I guess what I'm getting at is, the people producing content on the Internet and the people producing effective software aren't necessarily the same people. I mean, heck, look at me, I'm only here to type this comment because I'm slacking off at work today.

1: https://www.oreilly.com/radar/what-we-learned-from-a-year-of...