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How Machines Learned Meaning: The Evolution of Embeddings (2000–2026)
Tag: Learning In Public I have been diving deep into the world of Large Language Models (LLMs) recently. It is easy to get distracted by the flashy new models releasing every week, but to really unde
shalem-raju.hashnode.dev7 min read
#ai-history#artificial-intelligence#machine-learning#natural-language-processing#deep-learning#llms#generative-ai#vector-embeddings#rag#semantic-search
klement Gunndu
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.