Interesting. One problem I'm facing is using RAG to retrieve applicable rules instead of knowledge (chunks): only rules that may apply to the context should be injected into the context. I haven't done any experiment, but one approach that I think could work would be to train small classifiers to determine whether a specific rule could apply. The main LLM would be tasked with determining whether the rule indeed applies or not for the current context.

An example: let's suppose you're using an LLM to play a multi user dungeon. In the past your character has behaved badly with taxis so that the game has decided to create a rule that says that whenever you try to enter a taxi you're kicked out: "we know who you are, we refuse to have you as a client until you formally apologize to the taxi company director". Upon apologizing, the rule is removed. Note that the director of the taxi company could be another player and be the one who issued the rule in the first place, to be enforced by his NPC fleet of taxis.

I'm wondering how well this could scale (with respect of number of active rules) and to which extent traditional RAG could be applied. It seems deciding whether a rule applies or not is a problem that is more abstract and difficult than deciding whether a chunk of knowledge is relevant or not.

In particular the main problem I have identified that makes it more difficult is the following dependency loop that doesn't appear with knowledge retrieval: you need to retrieve a rule to identify whether it applies or not. Does anyone know how this problem could be solved ?