A quick thought on Retrieval-Augmented Generation (RAG) and its transformative potential.

In industries like aerospace, where vast amounts of unstructured data from decades of experience (e.g., Apollo missions) exist, the challenge isn’t just retrieving this data, but reviving its meaning. As experts retire, their knowledge is often left behind in documents and records, which retrieval systems alone can’t fully leverage.

RAG offers a solution by not only retrieving information but enhancing it with reasoning—almost as if you’re speaking to an expert. This blend of retrieval and generation is key to unlocking the true value of data and keeping vital expertise alive.

Imagine an AI agent that allows user to talk with and have direct access to response to any kind of question regarding the knowledge of the company. An AI agent that is literally able to mimic all the previous competences of the company, even 100 years ago.

How far away are we from this reality? 10 years ago probably we were very far away from this, but with the last advances in Large Language Models my feeling is that we made a giant leap in the last few years and this starts to be reality.

What is your though? How far are we from this giant leap?