Comment by soldeace

In a recent project I was asked to create a user story classifier to identify whether stories were "new development" or "maintenance of existing features". I tried both approaches, embeddings + cosine distance vs. directly asking a language model to classify the user story. The embeddings approach was, despite being fueled by the most powerful SOTA embedding model available, surprisingly worse than simply asking GPT 4.1 to give me the correct label.