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Embeddings are underrated (2024)
underrated§ Machine learning (ML) has the potential to advance the state of the art in technical writing. No, I’m not talking about text generation models like Claude, Gemini, LLaMa, GPT, etc.
The most famous example of the weird utility of this technology comes from the Word2vec paper, the foundational research that kickstarted interest in embeddings 11 years ago. The 2D map analogy was a nice stepping stone for building intuition but now we need to cast it aside, because embeddings operate in hundreds or thousands of dimensions. As docs site owners, I wonder if we should start freely providing embeddings for our content to anyone who wants them, via REST APIs or well-known URIs.
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