Daniel D. Gutierrez, Editor-in-Chief & Resident Data Scientist, insideAI News, is a practicing data scientist who’s been working with data long before the field came in vogue. He is especially excited ...

Through natural language queries and graph-based RAG, TigerGraph CoPilot addresses the complex challenges of data analysis and the serious shortcomings of LLMs for business applications. Data has the ...

Retrieval-augmented generation, or RAG, integrates external data sources to reduce hallucinations and improve the response accuracy of large language models. Retrieval-augmented generation (RAG) is a ...

For instance, a RAG model might reference different and incompatible repair manuals for different car models, or misquote legal provisions, confusing the meanings of prohibited, not recommended, and ...

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...

The problem: Generative AI Large Language Models (LLMs) can only answer questions or complete tasks based on what they been trained on - unless they’re given access to external knowledge, like your ...

Google published details of a new kind of AI based on graphs called a Graph Foundation Model (GFM) that generalizes to previously unseen graphs and delivers a three to forty times boost in precision ...