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AI Data: RAG is Dead, Long Live Memory
1 Jan
Summary
- RAG architecture faces limitations, evolving into enhanced approaches.
- Contextual memory is becoming essential for agentic AI workflows.
- PostgreSQL is the ascendant database for building GenAI solutions.
As agentic AI advances, the data infrastructure is undergoing rapid transformation. Traditional RAG pipelines, limited by static retrieval, are giving way to enhanced approaches and contextual memory, essential for AI that learns and adapts. While RAG won't disappear, contextual memory is becoming a foundational requirement for sophisticated AI workflows.
Purpose-built vector databases are also evolving. The integration of vector data types into multimodal databases and even object storage is reducing the need for specialized systems. However, niche use cases requiring peak performance will still favor dedicated solutions.
Meanwhile, PostgreSQL is emerging as the dominant database for GenAI. Significant investments in PostgreSQL vendors underscore its growing relevance due to its flexibility, open-source nature, and performance. Acquisitions and consolidation among data vendors are also expected to continue, shaping the future of AI-sustained data infrastructure.
