Grounding via RAG helped but not completely eliminating hallucinations

RAG usually improves grounding, but it does not make hallucinations disappear on its own. If the retrieved context is incomplete, noisy, or poorly ranked, the model can still fill in gaps with confident-sounding guesses.

This is why teams should treat retrieval and generation as separate quality problems. Better search, better chunking, and better context packaging often matter as much as the model itself.

Even with strong retrieval, some hallucinations remain because the system is still generating language rather than quoting facts. The goal is not perfect elimination, but much better control over when the model should answer and when it should abstain.