I find it helpful to distinguish between bugs and design flaws.
A bug is caused by a poorly implemented version of the design (or a literal bug in the system). Fixing a bug requires identifying where the system varies from the design and bringing it into alignment with the design.
A design flaw is a case where the idealized system as conceived by the engineers is incapable of fully solving the problem statement. Fixing a design flaw may require small tweaks, but it can also mean that the entire solution needs to be thrown out in favor of a new one.
Importantly, what's a design flaw for one problem statement may be just fine or even beneficial for another problem statement. So, more objectively, we might refer to these as design characteristics.
Hallucinations are a special case of two low-level design characteristics of LLMs: first, that they are trained on more data than can reasonably be filtered by a human (and therefore will be exposed to data that the humans wish it weren't) and second, that they produce their text by sampling a distribution of probabilities. These two characteristics mean that controlling the output of an LLM is very very difficult (or, as the article suggests, impossible), leading both to hallucinations and alignment concerns (which are actually the same concept framed slightly differently).
If the problem statement for an LLM application requires more 99% factual accuracy or more than 99% "doesn't produce content that will make investors nervous" accuracy, these design characteristics count as a design flaw.
If that's true for your use case (it's not true for all) then yes, including LLMs in your design would be a design flaw until they get much much better.
A bug is caused by a poorly implemented version of the design (or a literal bug in the system). Fixing a bug requires identifying where the system varies from the design and bringing it into alignment with the design.
A design flaw is a case where the idealized system as conceived by the engineers is incapable of fully solving the problem statement. Fixing a design flaw may require small tweaks, but it can also mean that the entire solution needs to be thrown out in favor of a new one.
Importantly, what's a design flaw for one problem statement may be just fine or even beneficial for another problem statement. So, more objectively, we might refer to these as design characteristics.
Hallucinations are a special case of two low-level design characteristics of LLMs: first, that they are trained on more data than can reasonably be filtered by a human (and therefore will be exposed to data that the humans wish it weren't) and second, that they produce their text by sampling a distribution of probabilities. These two characteristics mean that controlling the output of an LLM is very very difficult (or, as the article suggests, impossible), leading both to hallucinations and alignment concerns (which are actually the same concept framed slightly differently).
If the problem statement for an LLM application requires more 99% factual accuracy or more than 99% "doesn't produce content that will make investors nervous" accuracy, these design characteristics count as a design flaw.