Overview RAG is transforming AI apps, and vector databases are the engine behind accurate, real-time responsesChoosing the ...

Enterprise intent to adopt hybrid retrieval tripled from 10.3% to 33.3% in Q1 as first-gen RAG architecture failed at agentic ...

Open-source vector database startup Qdrant Solutions GmbH today announced three new enterprise-grade capabilities on its ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More More companies are looking to include retrieval augmented generation (RAG ...

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 ...

Data integration startup Vectorize AI Inc. says its software is ready to play a critical role in the world of artificial intelligence after closing on a $3.6 million seed funding round today. The ...

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 ...

In practice, retrieval is a system with its own failure modes, its own latency budget and its own quality requirements.

At AI Dev 26 x SF today, Actian, the data and AI division of HCLSoftware, announced Actian VectorAI DB, a portable vector database purpose-built to power production AI in regulated, disconnected, and ...

If you’re building generative AI applications, you need to control the data used to generate answers to user queries. Simply dropping ChatGPT into your platform isn’t going to work, especially if ...