Yugabyte announced it is releasing YugabyteDB 2026.1 with enhanced AI capabilities along with YugabyteDB AMP (Agentic Multitenant PostgreSQL) for true serverless, scale-to-zero PostgreSQL where every ...

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 ...

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a ...

Large Language Models (LLMs) have transformed how we interact with information. However, their reliance solely on internal knowledge can limit the accuracy and depth of their responses, especially ...

Retrieval-augmented generation (RAG) has emerged as a pivotal framework in AI, significantly enhancing the accuracy and relevance of responses generated by large language models (LLMs) leveraging ...

RAG is transforming AI apps, and vector databases are the engine behind accurate, real-time responses Choosing the right vector database can make or break performance, scalability, and user experience ...

Traditional RAG systems struggle bridging structured SQL databases and unstructured document collections (a challenge we call the modality gap), leading to incomplete reasoning and hallucinations.

What if your AI agent could not only answer your questions but also truly understand them, navigating complex queries with precision and speed? While the rise of vector search has transformed how AI ...