I think you're going about it backwards. You don't take a tool, and then try to figure out what to do with it. You take a problem, and then figure out which tool you can use to solve it.
But it seems to me that's what they're doing: "We have LLMs, what to do with them?" But anyway, I'm seriously just looking for an example of app that is build with stuff described in the article.
Me personally, I only used LLM for one "serious" application: I used GPT-3.5Turbo for transforming unstructured text into JSON; it was basically just ad-hoc Node.js script that called API (prompt was few examples of input-output pairs), and then it did some checks (these checks usually failed only because GPT also corrected misspellings). It would take me weeks to do it manually, but with the help of GPT it was few hours (writing of the script + I made a lot of misspellings so the script stopped a lot). But I cannot imagine anything more complex.
Since you seem to have not noticed my comment above, here's another example of a project that implements many of these techniques. Me and many others have used this to transcribe hour long videos into a well organized "docs site" that makes the content easy to read.
This was completely auto-generated in a few minutes. The author of the library reviewed it and said that it's nearly 100% correct and people in the company where it was built rely on these docs.
Tell me how long it would take you to write these docs. I'm really confused where your dismissive mentality is coming from in the face of what I think is overwhelming evidence to the contrary. I'm happy to provide example after example after example. I'm sorry, but you are utterly, completely wrong in your conclusions.
But that seems to belong to the category "text transformation" (e.g. translating, converting unstructed notes into structured data, etc.), which I acknowledge LLMs are good at; instead of category "I'll magically debug your SQL wish!".
I believe we were discussing the former not the latter? I agree that for lots of problem solving tasks it can be hit or miss - in my experience, all the models are quite bad at writing decent frontend code when it comes to the rendered page looking the way you want it to.
What you're describing is more about reasoning abilities - that's not really what the article was about or the problems the techniques are for. The techniques in article are more for stuff like Q&A, classification, summarization, etc.
I've tried this type of thing quite a bit (generating documentation based on code I've written), and it's generally pretty bad. Even just generating a README for a single source file project produces bloviated fluff that I have to edit rigorously. I'd say it does about 40% of the job, which is obviously a technical marvel, but in a practical sense it's more novelty than utility.
Please just go and try the lumentis library I mentioned - that is what was used to generate this. It works. For the library docs I showed, I literally just wrote a zsh script to concat all the code together into one file, each one wrapped with XML open/close tags, and fed that in. Just because you weren't able to do it doesn't mean it's a novelty.
