[amber_raza]

You hit the nail on the head regarding the 'semantic gap'.

Currently, I handle this via Smart Routing. The engine analyzes the intent of your query (e.g. identifying if you’re looking for an RCT, a specific guideline, or drug dosing) and routes it to the most relevant clinical database using high-precision keyword matching.

I chose this deterministic approach for the launch to ensure clinical precision. While vector/semantic search is great for general concepts, it can sometimes surface 'similar-ish' papers that miss the specific medical nuances (like a specific ICD-10 code or dosage) required for clinical evidence.

The LLM (Gemini 2.5 Flash) currently lives in the Synthesis Layer. It takes the raw, high-precision results and synthesizes them into the clinical summaries you see.

I actually have LLM-based query expansion (translating natural language into robust MeSH/Boolean strings) built into the infrastructure, but I am keeping it in 'staging' right now. I want to ensure that as I bridge that semantic gap, I don't sacrifice the deterministic accuracy that medical professionals expect.