Not confident if our embeddings are even useful how do you evaluate quality

Embeddings feel mysterious because they often power important parts of the system while staying invisible to most of the team. When retrieval works, everyone assumes the vectors are fine. When it fails, nobody is sure whether the problem is the embedding model, the index setup, or the evaluation method.

The easiest mistake is relying on abstract similarity scores and calling that proof of quality. Useful embeddings should be judged against real tasks: nearest neighbors that make sense, hard negatives that stay separated, and query-document matches that reflect actual user intent.

If you want confidence, create a small benchmark from real examples and measure how often the embeddings surface the right candidates. Once evaluation becomes task-based instead of purely mathematical, you get a much clearer picture of whether the vectors are helping or just looking sophisticated.