The Real Issue: Context Without Structure
Most teams plug RAG on top of a document dump and hope semantic search finds “relevance.” Without structure, relevance turns into noise.
When a human expert answers, they recall relationships. Policies with exceptions. Procedures with prerequisites. Causes with effects. RAG lacks this mental map.
Build a Domain Knowledge Graph first. Everything changes.
What a Domain Graph Actually Does
It organizes unstructured chaos into meaningful connections:
- Entities: who or what the data is about
- Relationships: how those entities interact
- Rules: what defines valid connections or exceptions
The Recommended Framework
- Map the domain: Identify key entities and relationships. Example in banking: customer, account, transaction, KYC flag.
- Link the source data: Connect documents, databases, and metadata to these nodes. Teach the system how information relates, not just how it looks.
- Then add RAG: After the structured layer is in place, plug in retrieval and generation. Now the model follows real connections instead of inventing them.
Why This Matters
RAG sounds production-ready, so teams rush it. Retrieval without reasoning is expensive autocomplete.
The next wave of enterprise AI moves from “semantic search” to knowledge-grounded reasoning. That shift starts with better graphs, not bigger clusters.
The Bottom Line
Do not feed your model more documents. Feed it understanding.
The real leap in retrieval will not come from better embeddings. It will come from a better context.
