Totally agree, RAG by itself isn’t enough — especially when users don’t follow the script.

We’ve seen similar pain: one-shot retrieval works great in perfect lab settings, then collapses once you let in real humans asking weird followups like

“do that again but with grandma’s style” and suddenly your context window looks like a Salvador Dali painting.

That branching tree approach you mentioned — composing prompt→prompt→query in a structured cascade — is underrated genius. We ended up building something similar, but layered a semantic engine on top to decide which prompt chain deserves to exist in that moment, not just statically prewiring them.

It’s duct tape + divination right now. But hey — the thing kinda works.

Appreciate your battle-tested insight — makes me feel slightly less insane.

haha fair — guess I’ve just been on the planet where the moment someone asks a followup like “can you explain that in simpler terms?”, the whole RAG stack folds like a house of cards.

if it’s been smooth for you, that’s awesome. I’ve just been chasing edge cases where users go off-script, or where prompt alignment + retrieval break in weird semantic corners.

so yeah, maybe it’s a timezone thing

agree — I’ve used Q/S with AI-assisted query shaping too, especially when domain vocab gets wild. the part I kept bumping into was: even with perfect-looking queries, the retrieved context still lacked semantic intent alignment.

so I started layering reasoning before retrieval — like a semantic router that decides not just what to fetch, but why that logic path even makes sense for this user prompt.

different stack, same headache. appreciate your insight though — it’s a solid route when retrieval infra is strong.