Fine-Tuning's Revenge: Why RAG Is Losing Ground in Production AI Systems The Hype Cycle Breaks RAG dominated early enterprise deployments because it promised quick wins without touching model weights. Yet the approach hit hard limits once teams moved beyond prototypes. Retrieval pipelines added layers of latency and introduced brittle failure modes when vector stores drifted from source data....