| Appreciate the transparency and the insight from a fellow builder. You are spot on that maintaining a fresh, high-quality index at scale is the 'hard problem' (and why tools like OpenEvidence are expensive). However, I found that for clinical queries, Vector/Semantic Search often suffers from 'Semantic Drift'—fuzzily matching concepts that sound similar but are medically distinct. My architectural bet is on Hybrid RAG: Trust the MeSH: I rely on PubMed's strict Boolean/MeSH search for the retrieval because for specific drug names or gene variants, exact keyword matching beats vector cosine similarity. LLM as the Reranker: Since API search relevance can indeed be noisy, I fetch a wider net (top ~30-50 abstracts) and use the LLM's context window to 'rerank' and filter them before synthesis. It's definitely a trade-off (latency vs. index freshness), but for a bootstrapped tool, leveraging the NLM's billions of dollars in indexing infrastructure feels like the right lever to pull vs. trying to out-index them. |