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Thanks.
And to clarify, other than the corporate greed is there any actual use case for the work around their limitations? I mean if the building materials aren't strong enough there is only so much you can achieve with a beautiful paint job (my current understanding, and I may be wrong)
The underlying issues, in my opinion, regarding LLMs is their indeterministic nature. Even zeroing out the temperature (randomness of outputs), you can get significantly different results between two almost identical texts.
However, building out an ecosystem supporting new technology is a fairly common progression. If you compare it to the internet things like browser caches, CDNs (content delivery networks), code minifiers, etc. are all ways to help combat latency (a fundamental problem for the internet).
As for the effectiveness of these solutions, RAGs do help a lot when generating text against a select corpus. Its what allows the linked sources in things like ChatGPT and Googles AI results. It's also what a lot of companies are using for searching their support pages/etc. It's maybe not quite as good as speaking to a person, but is faster.
Similarly, the reasoning models and managing the models "context" both have shown demonstrable improvements for models in benchmarking.
I'm not sure I personally believe this makes LLMs a replacement for humans in most situations, but it at least demonstrates forward progress for GenAI.
Interesting, the thing is I can quite easily pick up something new but at the same time I am very resistant to change until there is good reasoning and some sort of a scientific conformation.
Need to discover good uses cases for LLM/AI and make peace with it I guess!