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 ...
VentureBeat

Snowflake builds new intelligence that goes beyond RAG to query and aggregate thousands of documents at once

Enterprise AI has a data problem. Despite billions in investment and increasingly capable language models, most organizations still can't answer basic analytical questions about their document ...
ZDNet

Want generative AI LLMs integrated with your business data? You need RAG

RAG is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information ...
eWeek

What is Retrieval-Augmented Generation? How it Works & Use Cases

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...
Geeky Gadgets

RAG That Works : Mix Keyword, Graph, and SQL for Accurate Answers

What if your AI agent could not only answer your questions but also truly understand them, navigating complex queries with precision and speed? While the rise of vector search has transformed how AI ...
InfoWorld

Overcoming AI hallucinations with RAG and knowledge graphs

Generative AI depends on data to build responses to user queries. Training large language models (LLMs) uses huge volumes of data—for example, OpenAI’s GPT-3 used the CommonCrawl data set, which stood ...
Forbes

The Power Of RAG: How Retrieval-Augmented Generation Enhances Generative AI

Rahul is the Chief Product and Marketing Officer for Innodata, a global data engineering company powering next-generation AI applications. Generative AI is transforming industries and lives. It ...