False positives in a RAG search system for Instagram videos

The goal is to build a RAG pipeline that finds relevant Instagram creators from their videos. Example searches include “mom creator” or “curly-haired creator.” The current setup creates reel summaries with Gemini 3.1 Flash Lite, then turns those summaries into 384-dimension embeddings with BAAI/bge-small-en-1.5. It runs multi-query vector search in a database and ranks creators by confidence and coverage, meaning whether their surfaced reels pass a score threshold and how many of them pass it.

The main problem is false positives: creators who do not really match the search are still being returned. The system also lacks clear evaluation parameters, so there is no strong way yet to measure whether changes are actually improving the results.