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 ...
VentureBeat

Table-augmented generation shows promise for complex dataset querying, outperforms text-to-SQL

AI has transformed the way companies work and interact with data. A few years ago, teams had to write SQL queries and code to extract useful information from large swathes of data. Today, all they ...
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 ...
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 ...
VentureBeat

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

The vector database category is undergoing a shift in response to the needs of agentic AI. The retrieval-augmented generation (RAG)-to-vector database pipeline doesn't cut it anymore; agentic AI ...
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 ...
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 ...