I think one of key reasons why we have so many issues with LLMs is because there is no clear understanding of their limitations. This is how I see LLM limits:

1. AI is inconsistent and unpredictable. It can do what is expected today, but the same AI can do totally different, unpredictable, and maybe even dangerous thing tomorrow.

2. AI is bad on a long context - if you give AI instructions, it forgets/misses/misinterprets them. The longer the context, the more deviations are produced.

As a result, AI usage must follow these rules:

1. There should always be a human-in-the-loop to review every AI output - it solves problem #1. Yes, there will be a significant slowdown of the processes, but alternative is a quickly accumulating mess.

2. AI must be given only a short context to deal with - this is solution for problem #2. I predict this term will be popular soon - "AI microagent", meaning agent handling a short context. Yes, there will be a manual work integrating and orchestrating a huge number of these microagents, but it is still better than fixing all defects of AI-generated output after feeding it a single long context.