Hackaday

Air Canada’s Chatbot: Why RAG Is Better Than An LLM For Facts

Recently Air Canada was in the news regarding the outcome of Moffatt v. Air Canada, in which Air Canada was forced to pay restitution to Mr. Moffatt after the latter had been disadvantaged by advice ...

Databricks' Instructed Retriever beats traditional RAG data retrieval by 70% — enterprise metadata was the missing link

Instructed Retriever leverages contextual memory for system-level specifications while using retrieval to access the broader ...

Dify Makes Self-Hosted LLM Development Simple : Swap Models, Add RAG & Launch Faster

Self-host Dify in Docker with at least 2 vCPUs and 4GB RAM, cut setup friction, and keep workflows controllable without deep ...
VentureBeat

Why enterprise RAG systems fail: Google study introduces 'sufficient context' solution

A new study from Google researchers introduces "sufficient context," a novel perspective for understanding and improving retrieval augmented generation (RAG) systems in large language models (LLMs).
ZDNet

RAG can make AI models riskier and less reliable, new research shows

Retrieval-Augmented Generation (RAG) is rapidly emerging as a robust framework for organizations seeking to harness the full power of generative AI with their business data. As enterprises seek to ...
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

The limitations of model fine-tuning and RAG

The hype and awe around generative AI have waned to some extent. “Generalist” large language models (LLMs) like GPT-4, Gemini (formerly Bard), and Llama whip up smart-sounding sentences, but their ...