By combining RAG architectures, knowledge graphs, and vector databases, this approach bridges traditional data silos while ...

The latest releases showcase enhanced contextual AI, next-generation machine translation capabilities, and new integrations.

Retrieval-augmented generation represents a paradigm shift in AI-powered advertising, bridging the gap between creative ...

For years, search engines and databases relied on essential keyword matching, often leading to fragmented and context-lacking ...

To tackle data-retrieval-based hallucinations in non-diagnostic use cases, Mayo Clinic has applied CURE reverse RAG paired with vector databases.

Whether we should trust AI - particularly generative AI - remains a worthy debate. But if you want a better LLM result, you ...

Enhance RAG pipelines with DeepSeek R1 for precise, context-aware responses. Learn how to build a robust knowledge base and ...

English AI model developed by KISTI to provide real-time infectious disease monitoring, bridging language gaps in public ...

Contextual AI launches its Grounded Language Model (GLM) that achieves 88% factual accuracy, outperforming major competitors while minimizing hallucinations for enterprise applications.

Data integration platform provider Nexla Inc. today announced an update to its Nexla Integration Platform that expands ...

Its agentic RAG framework lets companies implement RAG for agents and assistants without coding and uses LLMs during each stage to improve accuracy. For example, Nexla can get context from multiple ...

The offering creates a single, trusted repository for all data and builds on the strong support Teradata offers today for retrieval-augmented generation (RAG), while working towards dynamic agentic AI ...