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 ...

This first article in a series explains the core AI concepts behind running LLM and RAG workloads on a Raspberry Pi, including why local AI is useful and what tradeoffs to expect.

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 ...

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 ...

We’ve been living through the generative AI boom for nearly a year and a half now, following the late 2022 release of OpenAI’s ChatGPT. But despite transformative effects on companies’ share prices, ...

Retrieval Augmented Generation (RAG) is supposed to help improve the accuracy of enterprise AI by providing grounded content. While that is often the case, there is also an unintended side effect.

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 ...

Large language models by themselves are less than meets the eye; the moniker “stochastic parrots” isn’t wrong. Connect LLMs to specific data for retrieval-augmented generation (RAG) and you get a more ...