InfoQ

Why Vector Search Alone Isn't Enough: Hybrid Retrieval for RAG

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

Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via ...
techtimes

How RAG and Vector Databases Improve LLMs

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 ...
Visual Studio Magazine

Empowering AI Applications with Vector Search in SQL Server and Azure Cosmos DB

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 ...
ZDNet

Microsoft Azure gets 'Models as a Service,' enhanced RAG offerings for enterprise generative AI

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 ...
Forbes

How RAG Continues To ‘Tailor’ Well-Suited AI

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 ...
VentureBeat

Microsoft boosts Azure AI Search with more storage and support for big RAG apps

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" ...