GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search ...

According to the latest analysis by Future Market Insights, the AI-Ready Enterprise Knowledge Graph Market is poised for exceptional growth as organizations ...

We are in an exciting era where AI advancements are transforming professional practices. Since its release, GPT-3 has “assisted” professionals in the SEM field with their content-related tasks.

RAG is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information ...

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

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 ...

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 ...

Imagine asking a question to your favorite AI assistant, only to receive an outdated or incomplete answer. Frustrating, right? Large Language Models (LLMs) are undeniably powerful, but they have a ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. In this eMag, we try to establish agentic AI ...