I’m setting up semantic search for internal documentation full of product names and team-specific terminology. General benchmark scores don’t tell me whether an embedding model will find the right page when someone searches using our own jargon.

How are you testing OpenAI embeddings for this kind of collection? I’m especially interested in practical ways to build a small evaluation set and decide whether the results are good enough before indexing everything.