LLMs look for the next highest probability token to output. It doesn't matter if it's 'true' in the knowledge sense. That's how you end up with a 'hallucination.' It's whatever is in the training data set. Throw in a small degree of 'randomness' each time so the answers aren't always the same, and you could end up with a good answer, a slightly wrong answer, or a wildly wrong one.

RAG embeddings limit the set of possible responses to what's in a document set. So there's a better chance you'll get something anchored in data you've provided, but it still relies on next-token probabilities so it can stick whatever token has the highest priority next.

What this Jev business does (AFAIK) is limit the response set. So the answer can be, say, one of four possible options, based on training data. It's much closer to how old expert systems worked, where you trained it on a large knowledge set and it tried to find the best path through heuristics. Here, since you've limited the answer space, it can also come back much faster, burning less GPU power, memory, etc.

I'm still trying to figure out the applications and pros and cons. Everyone should do their own research.