Beyond Proof-of-Concept: How to Build RAG Systems That Actually Scale

Building a Retrieval-Augmented Generation prototype is straightforward, but deploying it to production reveals serious operational challenges: unpredictable API token costs, high inference latency, and the silent risk of hallucinations. This deep dive explores a complete production architecture addressing these pain points through semantic caching, vector indexing optimization, and idempotent data pipelines. Teams scaling RAG systems will find proven engineering patterns and real solutions for cost-effective, reliable enterprise deployments.