Embeddings as features, not magic

An embedding turns text or another item into numbers that can be compared for similarity. That is useful for search, grouping, deduping, and recommendation. It is not magic truth. Similarity can surface useful candidates, but a human or a rule still needs to inspect whether the result fits the job.

A practical embedding artifact is a retrieval receipt: query, top matches, scores or ranks, metadata, and a reason each match deserves review.

For policy docs, research notes, or support issues, the builder question is: can someone inspect why this result appeared and decide whether to use it?