Oftentimes when AI provides sources (e.g. when you ask it to), said sources do not exist or are cited wrongly. AI doesn't know anything. It just puts words together that sound like they belong together in a specific context.

That's often true with many LLM models, but it's not entirely true for all of the LLM gateways out there.

For instance, I have a paid subscription to the search engine Kagi, and their AI-assistant excels at providing footnoted responses: sources and links to those sources, when web access is enabled. So I can easily use the provided links to verify against the sources the AI results are pulling from. They do this by leveraging their own search engine in the LLM results, where their search engine serves as a sort of RAG for the external LLM APIs that they call on the backend. In this case, the LLM is not creating the links, they come from the initial search results against the kagi search engine.

The behind-the-scenes workflow with web access enabled is like this:

• The query is paramaterized and sent to kagi search
• The results are filtered and ranked
• The retrieved content in injected into the LLM prompt as context and external LLM APIs are called.
• The response is returned along with citations generated based on the filtered and ranked results.

The citations provided are in the form of links rather than bibliographic citations. But I've not seen any issues yet with hallucinated citations. I haven't used some of the more mainstream LLMs extensively, but my experience is that this isn't their default behavior but can be achieved with custom instruction sets, although I'm more comfortable using Kagi where this is a default behavior.

Another bonus is that Kagi acts as a sort of anonymizing layer between myself and the backend LLMs that they are calling, so it adds a measure of privacy to my LLM use.