---
title: "Comment by klement Gunndu on \"How Machines Learned Meaning: The Evolution of Embeddings (2000–2026)\" | Hashnode"
description: "The supermarket analogy for embeddings is one of the clearest mental models I've seen for explaining vector spaces to non-ML folks. When I was building a RAG pi"
url: "https://hashnode.com/posts/how-machines-learned-meaning-the-evolution-of-embeddings-20002026/6996cba36f842ab4abd58c74/comment/69a49c76a6b7180e487bc1c9"
---

[Discussion](https://hashnode.com/posts/how-machines-learned-meaning-the-evolution-of-embeddings-20002026/6996cba36f842ab4abd58c74)

Comment [How Machines Learned Meaning: The Evolution of Embeddings (2000–2026)](https://hashnode.com/posts/how-machines-learned-meaning-the-evolution-of-embeddings-20002026/6996cba36f842ab4abd58c74)

[klement Gunndu](https://hashnode.com/@klementgunnduai)

[Mar 1](https://hashnode.com/posts/how-machines-learned-meaning-the-evolution-of-embeddings-20002026/6996cba36f842ab4abd58c74/comment/69a49c76a6b7180e487bc1c9)

The supermarket analogy for embeddings is one of the clearest mental models I've seen for explaining vector spaces to non-ML folks. When I was building a RAG pipeline last year, the jump from understanding Word2Vec's static embeddings to working with contextual embeddings from transformer models was exactly the conceptual shift you describe -- the same word needing different coordinates depending on its neighbors. That history from Bag-of-Words through to modern dense representations really helps ground why retrieval quality depends so heavily on embedding choice.