Better stated - we always want to improve our vector databases, but I tend to follow a rigid workflow for doing this. I wrote about the general process here.
Airtable my company, we gather analytics about questions put forth to the embedding system and how well the results perform. When we see a list of hits that have a relatively low similarity threshold, it’s an indication that the corpus may be lacking in some way. My approach makes this simple to identify, and my app makes it even easier to embellish the corpus.
I don’t, but I will say that any specific high-similarity match is not always a good (or complete) answer. Sometimes it requires mashing up the top three hits that are sent to GPT with the intent of summarizing the results from a range of relevant texts in the corpus.