How we helped a healthcare startup increase patient onboarding by 180%arrow_right_alt
RAG with reranking

Cast a wider net, then re-score it.

Instead of trusting embedding similarity alone, this pulls a wider pool of candidate passages, then a cross-encoder reranker scores each one against the question directly — a slower but more accurate second pass. The top three reranked passages each generate their own answer, so you can compare which passage actually answered the question best.

Your document is indexed on the first question; change the text and it re-indexes.
schedule Run time ~15 MB first run for the reranker, plus the embedding and drafting models
RELATED WORK

More Real-World Impact.

Contact us

Have a problem worth getting right?

Send us the paragraph you'd normally write to a colleague. We'll get back to you within 24 hours.

mailhello@tathastha.com