Much of the interest surrounding artificial intelligence (AI) is caught up with the battle of competing AI models on benchmark tests or new so-called multi-modal capabilities. But users of Gen AI's ...

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).

Performance. Top-level APIs allow LLMs to achieve higher response speed and accuracy. They can be used for training purposes, as they empower LLMs to provide better replies in real-world situations.

A consistent media flood of sensational hallucinations from the big AI chatbots. Widespread fear of job loss, especially due to lack of proper communication from leadership - and relentless overhyping ...

Also: Make room for RAG: How Gen AI's balance of power is shifting For that reason, researchers at Amazon's AWS propose in a new paper to set a series of benchmarks that will specifically test how ...

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 ...

RAG can make your AI analytics way smarter — but only if your data’s clean, your prompts sharp and your setup solid. The arrival of generative AI-enhanced business intelligence (GenBI) for enterprise ...