The article explores the challenges of implementing Retrieval-Augmented Generation (RAG) in enterprise environments, revealing that over 50% of deployments fail due to retrieval latency, data governance issues, and scalability problems. It introduces the Retrieval Fabric framework as a solution, emphasizing its event-driven indexing and hybrid retrieval strategies to integrate RAG seamlessly into data platforms. Author Anil Kumar Kandalam explains how treating RAG as a standalone component rather than a core data platform capability leads to systemic failures. This tutorial is worth reading because it provides actionable architectural patterns to overcome RAG’s limitations. Readers will learn to implement the Retrieval Fabric framework, including event-driven reindexing, chunk-level access control, and query-dependent retrieval depth.
Key facts
- Over 50% of RAG implementations fail due to retrieval latency, stale data, and governance gaps.
- Vector databases lack integration with enterprise data lifecycle management, causing outdated results and compliance risks.
- The Retrieval Fabric framework combines event-driven indexing, hybrid retrieval, and governance to address RAG shortcomings.
- Seven operational patterns include caching strategies, query-dependent depth adjustment, and chunk-level access control propagation.
- RAG’s success depends on treating it as a first-class data platform workload with observability and data lineage tracking.
