It is both subtle and obvious, yet many are missing this: if you want/need a deep subject matter expert in virtually any subject, write a narrative biography describing your expert using the same language that expert would use to describe themselves; this generates a context within the LLM carrying that subject matter expertise, and now significantly higher quality responses are generated. Duplicate this process for several instances of your LLM, creating a home brewed collection of experts, and have them collectively respond to one's prompts as a group privately, and then present their best solution. Now there is a method of generating higher reliability responses. Now turn to the fact that the LLMs are trained on an Internet corpus of data that contains the documentation and support forums for every major software application; using the building blocks described so far, it is not difficult at all to create agents that sit between the user and pretty much every popular software application and act as co-authors with the user helping them use that application.
I have integrated 6 independent, specialized "AI attorneys" into a project management system where they are collaborating with "AI web developers", "AI creative writers", "AI spreadsheet gurus", "AI negotiators", "AI financial analysts" and an "AI educational psychologist" that looks at the user, the nature and quality of their requests, and makes a determination of how much help the user really needs, modulating how much help the other agents provide.
I've got a separate implementation that is all home solar do-it-yourself, that can guide someone from nothing all the way to their own self made home solar setup.
Currently working on a new version that exposes my agent creation UI with a boatload of documentation, aimed at general consumers. If one can write well, as in write quality prose, that person can completely master using these LLMs to superior results.
>I have integrated 6 independent, specialized "AI attorneys" into a project management system where they are collaborating with "AI web developers", "AI creative writers", "AI spreadsheet gurus", "AI negotiators", "AI financial analysts" and an "AI educational psychologist" that looks at the user, the nature and quality of their requests, and makes a determination of how much help the user really needs, modulating how much help the other agents provide.
Ah yes, "it's so obvious no one sees it but me". Until you show people your work, and have real experts examining the results, I'm going to remain skeptical and assume you have LLMs talking nonsense to each each other.
The point is these characters are not doing the work for people, it co-authors the work with them. It's just like working with someone highly educated but with next to no experience - they're a great help, but ya gotta look at their work to verify they are on track. This is the same, but with a collection of inexperienced phds. The LLMs really are idiot savants, and when you treat them like that they respond with expectations better.
How can no one see what we have today? You only need six instances of an LLM running at the same time, with a system to coordinate between them, and then you have to verify the results manually anyway. Sign me up!
If a certain percent of the work is completed through research synthesis and multiple perspective alignment, why is said novel approach not worth lauding?
I've created a version of one of the resume GPTs that analyses my resume's fit to a position when fed the job description along with a lookup of said company. I then have a streamlined manner in which it points out what needs to be further highlighted or omitted in my resume. It then helps me craft a cover letter based on a template I put together. Should I stop using it just because I can't feed it 50 job roles and have it automatically select which ones to apply to and then create all necessary changes to documents and then apply?
but at some point, probably in the near future, they will. And then this system I have will already be in place, and that added capability will just arrive and integrate into all the LLM integrated systems I've made and they'll just improve.
I agree with OP, I think we still have no idea yet what dreams may come of the LLM's we have today. So no one will be able to "enlighten us" — perhaps not until we're looking in the rear-view mirror.
Enlighten us