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 ...
InfoWorld

AWS aims to take the pain out of RAG with Bedrock Managed Knowledge Base

The new service automates embeddings, indexing, and connectors to help developers focus on building AI apps instead of ...
TechCrunch

Why RAG won’t solve generative AI’s hallucination problem

Hallucinations — the lies generative AI models tell, basically — are a big problem for businesses looking to integrate the technology into their operations. Because models have no real intelligence ...
Computerworld

Despite its ubiquity, RAG-enhanced AI still poses accuracy and safety risks

Though Retrieval-Augmented Generation has been hailed — and hyped — as the answer to generative AI's hallucinations and misfires, it has some flaws of its own. Retrieval-Augmented Generation (RAG) — a ...
Forbes

The RAG Effect: How AI Is Becoming More Relevant And Accurate

The rapid advancements in artificial intelligence (AI) have led to the development of powerful large language models (LLMs) that can generate human-like text and code with remarkable accuracy. However ...