In RAG systems with high‑dimensional data, how do you balance vector search relevance against the need for specific key precision?

High‑dimensional vectors can yield high recall but low precision. I use a hybrid approach: first, perform a coarse vector search to retrieve the top‑k candidates. Then apply a metadata filter that enforces key constraints (e.g., document type, date range). After filtering, I re‑rank the remaining items using a lightweight re‑ranking model (e.g., a linear SVM or a small transformer) that incorporates both semantic similarity and key‑field relevance. I tune the trade‑off by adjusting the k value and the weight given to the metadata score. Evaluation uses precision@k and recall@k curves; I aim for a precision of at least 0.8 while keeping recall above 0.6. This strategy ensures that the system returns highly relevant documents that also satisfy strict key criteria.