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This paper analyzes the performance gap between prototype and production RAG systems, attributing it to data staleness, tenant data leakage, and query composition explosion stemming from split-system data layers. It proposes a unified data layer built on PostgreSQL with pgvector and HNSW indexing to address these issues. Benchmarks on 50,000 documents demonstrate significant latency reductions (up to 92%) and the elimination of data leakage compared to conventional approaches.
Ditch the brittle RAG stack: a unified PostgreSQL data layer slashes latency by up to 92% and eliminates data leakage, making production RAG finally reliable.
Retrieval-Augmented Generation (RAG) systems have become the standard architecture for grounding large language models in organizational knowledge. Yet production deployments consistently expose a gap between clean prototype performance and real-world reliability. This paper identifies three root causes of that gap: data staleness, tenant data leakage, and query composition explosion. All three trace back to the conventional split-system data layer. We propose and evaluate a unified data layer built on PostgreSQL with native vector search (pgvector) and HNSW indexing. Controlled benchmarks on 50,000 documents show 92% latency reduction for date-filtered queries, 74% for tenant-scoped queries, zero synchronization inconsistency, and complete elimination of cross-tenant data leakage with 93% less synchronization code. We additionally discuss a recommended hybrid tier architecture
