Building a Multi-Agent System from Scratch: Architecture Patterns

  > The problem is there's no signal for the recipient that the value being delivered aligns with the intention of the human who set the prompting chain into action, right?

We don’t even know what iterations, granted, if any, were performed on the article text before it was published.

Genuine question - is it? Why? If I set out to write a mediocre guide to bicycle maintenance, and through a spectacular series of typos I accidentally write a recipe for a sublime cheesecake, it's still true that I've written a good recipe, even if that wasn't my intent.

(Hopefully we can bypass quibbling about the use of the term "write" in an LLM-assisted context. Readers should feel free to substitute the clunkier phrase "take actions that cause words to be recorded" if they wish)

> It's [...] very difficult to figure out where to "listen"

I don't agree. Or rather, I don't think it's any harder than it has been since the Internet exploded, long before LLMs. It's just based on track record. Read an article; note that it's useful; note the source. See another article by that source; find it useful again; begin to attribute trust to them. Read a less-good article by them; lessen your trust somewhat.

By all means, observe "this person probably didn't edit this after an LLM generated it" and take that as an input to whether you will trust their observations - but you should probably be verifying anything technical that you read anyway. If you consistently observe unhelpful or misleading content from a particular source, you should stop trusting it, regardless of whether that content was human- or AI-generated.

> If an author doesn't try to make "listening" easier, then it puts the value they hold into question if they don't care about it enough themselves.

Again, I don't think that's true. If someone has tweaked and moulded an LLM-content-generation pipeline such that they can generate a piece of technical content that is still genuinely-useful even without a human editing pass to reduce LLM-isms, they're still a useful source. I might justifiably think they're lazy, uninvested, or not an expert in the domain - but it would be foolish to deny the usefulness of their output.

To put it differently - Michael Jordan could wake up hungover and, with one hand behind his back, still kick my ass at basketball. The fact that he's not trying the hardest he could doesn't mean that the scoreboard would be any less valid. Equivalently, caring about the content you create doesn't directly affect the value of what you create (again, I can't stress enough - for technical content only!) - it's probably positively correlated (because it will cause you to take more care), but _not_ a prerequisite.

For example, a small fast model watching another model's CoT stream to annotate it - highlighting and summarizing for me, or flagging intervention conditions.