How We Built a Custom RAG Pipeline to Generate Metadata Automatically

Most “AI in pharma” conversations skip the boring part.

They are metadata problems.

In commercial analytics, speed breaks the moment you cannot answer basic questions:

– Which HCP table is the right one?
– What does status_cd actually mean in this view?
– Is this a payer field, a plan field, or a coverage field?
– Can this metric be used for incentive comp, or only for directional reads?

When those answers live in people’s heads, every workflow slows down. Self serve BI. Territory and roster ops. Market access pull through. Omnichannel measurement. Forecast discussions. Even basic QA.

Mohit (intern) and I built a low-cost internal POC that generates metadata automatically across our warehouse, then stores it in a single metadata table we can treat as a semantic layer.

We made it comprehensive on purpose. About 100 views across sales performance, market access, engagement, claims, specialty pharmacy, EMR, HCP and account masters, and internal ops views like alignment and targets.

Not because “coverage” sounds good, but because commercial questions do not respect domain boundaries.

We also designed for realism: grounded retrieval from our own dictionaries and abbreviations, strict filtering to reduce hallucinations, and consistency checks so the same field does not get described five different ways.

#PharmaAnalytics #CommercialOperations #DataGovernance #Metadata #SemanticLayer #GenAI

I’m linking the write-up here. Check it out:
https://lnkd.in/gG3tyyp6