Embeddings Are Underrated
a year ago
- #technical writing
- #machine learning
- #embeddings
- Embeddings are a powerful ML technology for technical writing, enabling discovery of connections between texts at scale.
- Embeddings convert text into arrays of numbers, allowing mathematical comparison of any two pieces of text regardless of size.
- The output array size depends on the model used, with numbers representing semantic positions in a multi-dimensional latent space.
- Embeddings can be generated easily using services like Gemini or Voyage AI, with varying input limits and computational costs.
- Applications include semantic similarity comparisons, clustering related documents, and enhancing technical documentation maintenance.
- The concept of latent space allows embeddings to represent semantic relationships intuitively, like analogies (king - man + woman ≈ queen).
- Technical writers can use embeddings to recommend related content, improve documentation structure, and enable community-driven innovations.
- A Sphinx extension example demonstrates generating and comparing embeddings for documentation pages to find semantically related content.