Related documents aside, technical documentation benefits from really great search.
Embeddings are a _very_ useful tool for building better search - they can handle "fuzzy" matches, where a user can say things like "that feature that lets me run a function against every column of data" because they can't remember the name of the feature.
With embeddings you can implement a hybrid approach, where you mix both keyword search (still necessary because embeddings can miss things that use jargon they weren't trained on) and vector similarity search.
I wish I had good examples to point to for this!