Most RAG pipelines fall apart at scale.

They work great in a demo with 50 documents. Then someone points them at 10 million, and suddenly the model is hallucinating, retrieval is slow, and nobody trusts the answers anymore.
The fix isn't a bigger model — it's a better pipeline architecture. Four principles make the difference:
→ Retrieve with precision, not just recall
→ Constrain the model to only use retrieved context
→ Verify outputs against source documents before returning them
→ Abstain when confidence is low, instead of guessing
This mirrors what I keep seeing in applied AI work: the hard problems aren't in the model itself, they're in the system around it — retrieval quality, grounding, and knowing when not to answer.
If you're building anything RAG-based at scale, this framework is worth studying closely.