I contributed technical content and comments to an article titled “RAG (Retrieval-Augmented Generation) for Not-Quite-Dummies” on the Pure AI web site. See https://pureai.com/articles/2025/10/01/rag-retrieval-augmented-generation-for-not-quite-dummies.aspx.

The first part of the article explains how RAG works using a diagram. Proprietary documents are uploaded into a vector database. When a user queries the LLM+RAG system, the query first goes to the vector store, and relevant information related to the query is extracted. Behind the scenes, the vector store looks at chunks of embedded information, speeding up the process.

The relevant information from the vector store is combined with the user query and sent to the LLM. The LLM uses its knowledge of grammar and its own knowledge base to generate a response.

The second part of the article presents a concrete example program. The output of the example is:

Begin simple RAG demo

Creating demo vector store
Done. Store ID = vs_68d6a1xxxxxxxxxxxxxxxxxxbe26d92a

Uploading a text file to vector store

The query is:
What is the diameter and nickname of the planet Mercury?

The response is:
The nickname of Mercury is the "grape planet." However,
the diameter of Mercury is not provided in the file you
uploaded. If you need the diameter, the generally accepted
value is about 4,880 kilometers. The nickname, according
to your file, is "grape planet" due to its purple surface.

Deleting demo vector store

End RAG demo

I provide some comments.

McCaffrey observed, “The latest versions of APIs for GPT, Llama, Gemini, and other LLMs make it much easier to implement a RAG system than it used to be. As recently as just a few months ago, deploying a RAG system was much more difficult because the vector database and various connectivity components had to be implemented separately. But now APIs integrate RAG functionality into small, easy-to-use modules.”

He added, “One possible downside to RAG systems such as the one described in this article is security. However, companies that offer LLM services are well aware of security issues and there are ways to deal with most scenarios.”

Cyborgs are hardware-augmented humans. Here are three memorable (to me anyway) evil cyborgs.

Right: In “Spiderman 2” (2004), Dr. Otto Octavius designs a harness with four powerful mechanical tentacles controlled by AI. Bad idea because the AI takes over.