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In the constantly changing field of data management, the demand for effective and expandable storage options has become crucial. As businesses and technologies grow, so does the volume of data that needs to be processed and stored.  Traditional database systems often  struggle  to keep up with the demands of modern data loads, especially when it comes to complex data types like images, videos, and large-scale  machine-learning  models.   This  is where Rag Vector Database Technology comes into play, revolutionizing how we think about data storage and retrieval. The Emergence of Vector Databases Vector databases represent a significant shift from conventional relational database systems. Unlike traditional databases that store data in rows and columns, vector databases use vectors—a sequence of numbers  that represent  data in high-dimensional space. This approach is  particularly useful  for applications that involve artificial int...

Retrieval Augmented Generation Guide Today we posted a new guide explaining Retrieval Augmented Generation (RAG) over on Vectorize. Key points and take aways are: RAG helps reduce LLM hallucinations. RAG allows your LLM to generate content about topics it wasn't trained on. RAG works around training date cutoff problems by providing more up to date information. For any developers building gen AI features into their applications, this is a must know technique.  Visit Vectorize to learn more about how to build real time data pipelines optimized for RAG.