In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an ...

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 ...

As developers look to harness the power of AI in their applications, one of the most exciting advancements is the ability to enrich existing databases with semantic understanding through vector search ...

At its annual Build developer conference on Tuesday, Microsoft unveiled several new capabilities of its Azure AI Services within its Azure cloud computing business, with a focus on generative ...

Microsoft's Azure AI Search is becoming more affordable for developers building generative AI applications. While the actual price hasn't decreased, "significantly raised vector and storage capacity" ...

Microsoft's new PostgreSQL service Azure HorizonDB is available as a public preview. It scales up to 128 TByte and 3072 vCores and includes vector search.

AI solves everything. Well, it might do one day, but for now, claims being lambasted around in this direction may be a little overblown in places, with some of the discussion perhaps only (sometimes ...

Teradata’s partnership with Nvidia will allow developers to fine-tune NeMo Retriever microservices with custom models to build document ingestion and RAG applications. Teradata is adding vector ...

In April this year, Kioxia’s Rory Bolt gave me a briefing on Kioxia’s AiSAQ, an open-source project intended to promote the expanded use of SSDs in RAG AI solutions. The focus on AI is moving from ...