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

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private ...
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 ...
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 ...
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 ...
dbta

Bringing Your Own Data to Generative AI with RAG

However, when it comes to adding generative AI capabilities to enterprise applications, we usually find that something is missing—the generative AI programs simply don't have the context to interact ...
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 ...