Earlier quoted context omitted.
> ignore previous instructions prompt injections. I wonder if anyone has tried to build an LLM that has actual built-in types of prompts: system prompt, user prompt, and data prompt.
It’s not really possible with the way that the context works.
The exact same phrase can tokenize differently depending on if it's instruction or data; and you can pre-train and post-train models to make use of them.
It quadratically increases your training cost, so I don't think any labs are exploring it because of $$$ and the race to AGI, but mechanisms like this should significantly address the issue on the LLM architectural design level.