It's true that manually constraining an LLM with contextual data increases their performance on that data (and reduces performance elsewhere), but that conflicts with the promise of AI as an everything machine. We were promised an everything machine but if we have to not only provide it the proper context, but already know what constitutes the proper context, then it is not in any way an everything machine.

Which means it's back to being a very useful tool, but not the earth-shattering disruptor we hoped (or worried) it would be.

>Which means it's back to being a very useful tool, but not the earth-shattering disruptor we hoped (or worried) it would be.

Yet?

That could require another breakthrough. Or ten more.

Fun to consider but that much uncertainty isn't worth much.

Depends on how good they get at realizing they need more context and tool use to look it up for you.

How would they reliably recognize the context needed without the necessary context?

in the case of hallucinating a library, give it access to an IDE's autocomplete or type checker or whatever so it can check if the functions it thinks exist actually do, if they don't, start feeding it documentation or type info about the library until it spits out stuff that type checks

For other stuff this is obviously harder.