The role of Retrieval-Augmented Generation (RAG) in AI is being heavily debated as 2026 approaches, with many questioning its long-term viability in its current form. This debate stems from limitations inherent in the original RAG pipeline architecture, which resembles a basic search function, according to industry analysts.

The core issue is that RAG, as initially conceived, retrieves results tied to specific queries at specific points in time. Furthermore, early RAG pipelines, prevalent before June 2025, often operated with a single data source. This has led numerous vendors to suggest that RAG is becoming obsolete.

For decades, relational databases like Oracle dominated the data landscape, organizing information into rows and columns. However, this stability has been disrupted by the emergence of NoSQL document stores, graph databases, and, more recently, vector-based systems. The rise of agentic AI has accelerated the evolution of data infrastructure, making it more dynamic than ever before.

A key takeaway from 2025 is the increasing importance of data in the age of AI. The limitations of early RAG implementations highlight the need for more sophisticated approaches to data retrieval and integration. The future of vector databases and other data storage and retrieval methods will likely be shaped by the need to overcome these limitations and support more complex AI applications.