The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next

Vector database pioneer Pinecone recognizes this and is pivoting to meet the specific needs of agentic AI. The company today announced Nexus, which it positions as a knowledge eng ...
Spiceworks on MSN

Beyond RAG as a buzzword: Aligning AI retrieval with real-world use

RAG talk is prevalent, yet simply stating “we used RAG” fails to provide much information about its effectiveness in a production environment. A more effective approach is to examine how you align ...

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 ...
dbta

RESEARCH@DBTA: Survey: RAG Emerges as the Connective Tissue of Enterprise AI

While the generative AI (GenAI) revolution is rolling forward at full steam, it’s not without its share of fear, uncertainty, and doubt. The great promises that can be delivered through large language ...
CIO

Vector Database vs. Knowledge Graph: Making the Right Choice When Implementing RAG

Whether IT leaders opt for the precision of a Knowledge Graph or the efficiency of a Vector DB, the goal remains clear—to harness the power of RAG systems and drive innovation, productivity, and ...
dbta

A Deep Dive into RAG Adoption and Optimization with Elastic

Retrieval-augmented generation—or RAG—is an AI strategy that supplements text generation with information from private or proprietary data sources, according to Elastic, the search AI company. RAG ...
InfoWorld

What is retrieval-augmented generation? More accurate and reliable LLMs

Retrieval-augmented generation, or RAG, integrates external data sources to reduce hallucinations and improve the response accuracy of large language models. Retrieval-augmented generation (RAG) is a ...