The role of Retrieval-Augmented Generation (RAG) in AI is being heavily debated as 2026 approaches, with some vendors claiming the original RAG pipeline architecture is becoming obsolete. This shift is driven by the limitations of early RAG systems, which functioned much like basic search engines, retrieving results for specific queries at specific points in time, often limited to a single data source.
For decades, the data landscape remained relatively stable, dominated by relational databases. However, the rise of NoSQL document stores, graph databases, and, most recently, vector-based systems, has disrupted this stability. According to Sean Michael Kerner, writing in VentureBeat at the close of 2025, the era of agentic AI is causing data infrastructure to evolve at an unprecedented pace.
The core issue with early RAG pipelines, as Kerner noted, is their similarity to simple search functions. They retrieve information based on a specific query at a particular moment. This contrasts with the demands of modern AI applications that require more dynamic and comprehensive data integration.
The limitations of RAG have spurred innovation in data infrastructure. The focus is shifting towards systems that can handle multiple data sources and adapt to evolving information needs. This evolution reflects a broader recognition that data is more critical than ever in the age of AI.
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