You can do it the same way human does: recall a fact from memory, and then search to identify a citation. The citation isn't "here's why I think this" but instead "here's where you can verify this".

For standard responses you're not wrong, but increasingly there are a lot of LLMS that are essentially doing RAG against search results. For example this is I believe how Kagi works, and Google AI overviews. I have increasingly seen Claude and chat GPT also doing the same thing where instead of answering a question from the knowledge Bank they will do a web search and cite the responses that were used.

> It is perfectly common to find that a "source" does not contain the statements.

It is commonly this way right now, but it doesn't have to stay this way forever.

It is also common, in my usage at least, to iteratively brow-beat the bot into paring its statements down to those that which are supportable by its sources. Doing so just takes repetition, and that repetition takes time and burns more tokens.

With the present state of things, the prompts to get moving on this and to guide the ultimate response into something that is verifiably supportable by outside sources can usually be simple and largely generic.

They're easy enough prompts that a subagent can produce them.

(I've done it myself with Codex subagents and it worked very well, aside from the unsustainable burn rate that did not fit my budget.)

This is why it's dumb when inferencing with a model to ask "...and cite all sources". Total waste of time and likely to make the response worse.

BUT

On models with web search, they can scan the sources in context, and those are pretty good at correct citations. However it's still a "trust, but verify" situation.

This is true, but good LLMs (even local, consumer-sized ones these days) can do this much better than … whatever it is that Google’s AI overviews use.

Think about your coding harness: the model reads a bunch of files into the context, and it generally doesn’t forget/hallucinate which lines came from which file.

Are you sure? Because this sounds like GPT3-era understandings of LLM-isms

Then the least they can do is cite ALL their sources. At least somewhere on their website.

99% of AI users these days aren't using a 'raw' LLM. They are interacting with a harness that includes tools to do search of the live (or recently crawled) web. And so the output users actually see could absolutely include correct citation of sources. So the 'architecturally impossible' bit may be technically correct but it is not practically relevant. Now whether those harnesses do a good job of orchestrating LLM output to get accurate citations (and whether they are transparent about their process) is another thing entirely. But if you're contemplating 'what LLM's can do' and aren't taking into account the harness and tooling ecosystem they are embedded in then you're missing the point.

> It is architecturally impossible for an LLM to associate a link or citation that it crawled with a response that comes out the other end.

I mean, its not. Lots of LLM's focused on finance do this already.

Bloomberg's own ASKB produces results and provides links back to the source documents or urls that it references so people who care about correctness can verify the results.

Lol, this same behavior comes up more often than you'd expect in academia too. Many many citations that nobody cross checks that also don't support the originally cited piece of information.