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Next-generation database reduces AI hallucinations and improves accuracy by 78%

One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is "hallucination"—the generation of plausible-sounding but factually incorrect information. KAIST ...

KAIST: "Improving Data Processing Accuracy and Speed, Key to Overcoming Barriers to Enterprise AI Adoption"

Artificial Intelligence (AI) agents based on Retrieval-Augmented Generation (RAG) technology are rapidly proliferating. RAG ...
techtimes

How RAG and Vector Databases Improve LLMs

Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) are two distinct yet complementary AI technologies. Understanding the differences between them is crucial for leveraging their ...
Mirage News

KAIST Unveils Database Tech Cutting AI Errors by 78%

One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is "hallucination" — the generation of ...
VentureBeat

The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next

The vector database category is undergoing a shift in response to the needs of agentic AI. The retrieval-augmented generation (RAG)-to-vector database pipeline doesn't cut it anymore; agentic AI ...
InfoWorld

Teradata adds Enterprise Vector Store to augment RAG

Teradata’s partnership with Nvidia will allow developers to fine-tune NeMo Retriever microservices with custom models to build document ingestion and RAG applications. Teradata is adding vector ...