Author(s): Shenggang Li

Originally published on Towards AI.

Why “LLM + Retrieval + Private Data” collapses outside the demo — and what real AGI-like systems actually require

Over the last two years, a new corporate myth has spread faster than any technical breakthrough:

The article discusses the misconceptions surrounding the idea that integrating Retrieval-Augmented Generation (RAG) with large language models (LLMs) and private data equates to achieving Artificial General Intelligence (AGI). It emphasizes the structural differences between open-loop and closed-loop systems, illustrating how real AGI requires verifiable actions and dynamic interaction with the world, rather than static retrieval capabilities that can produce outdated or inaccurate results. The author argues that current implementations may lead to negative value systems, highlighting the importance of frameworks like those seen in successful AGI-like systems, which integrate real-time data, actions, and effective governance. Ultimately, RAG is framed as a transitional tool rather than a final solution for AGI development.

“`Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI