Training a Powerful Text Encoder

Text encoders are the quiet workhorses behind search, retrieval, clustering, and RAG: models that map a piece of text to a single vector where distance carries meaning. Unlike a generative decoder, the goal isn't to predict the next token. It's to land semantically similar inputs near each other and push unrelated ones apart.

This post is the start of a series on training one from scratch: the architecture choices, the contrastive objective, how negatives are mined, and how to evaluate the embeddings once you have them.

More coming soon.