Right... knowledge is one of the things (the one thing?) that LLMs are really horrible at, and that goes double for models small enough to run on normal-ish consumer hardware.
Shouldn't we prefer to have LLMs just search and summarize more reliable sources?
Even large hosted models fail at that task regularly. It's a silly anecdotal example, but I asked the Gemini assistant on my Pixel whether [something] had seen a new release to match the release of [upstream thing].
It correctly chose to search, and pulled in the release page itself as well as a community page on reddit, and cited both to give me the incorrect answer that a release had been pushed 3 hours ago. Later on when I got around to it, I discovered that no release existed, no mention of a release existed on either cited source, and a new release wasn't made for several more days.
Reliable sources that are becoming polluted by output from knowledge-poor LLMs, or overwhelmed and taken offline by constant requests from LLMs doing web scraping …
Try to push your point to absurd you see why; hint - to analyze data pulled by tools you need knowledge already baked in. You have very limited context, you cannot just pull and pull data.
