How should RAG and a semantic layer work together?

Summary

• RAG (retrieval-augmented generation) retrieves relevant context from documents; a semantic layer ensures that context is meaningful and aligned with business definitions.
• The semantic layer transforms RAG from generic document retrieval into enterprise-aware reasoning by grounding retrieved context in governed definitions, relationships, and permission-aware access.
• Genie Ontology combines automatic context extraction from your existing Databricks assets with governed semantic grounding through Unity Catalog, metric views, domains, and Pages.
• Without a semantic layer, RAG can retrieve documents but still misunderstand the enterprise. Retriever quality, guided by semantic structure, is the single biggest determinant of RAG output quality.

How should RAG and a semantic layer work together?
RAG and a semantic layer are complementary but distinct. RAG retrieves relevant context from a knowledge base, while a semantic layer ensures that the retrieved context is meaningful and aligned with how your enterprise defines and relates its concepts. Together, they enable AI systems to reason accurately about your data rather than hallucinate or misinterpret.
Without a semantic layer, RAG is a generic document-retrieval system. With one, RAG becomes an enterprise-aware reasoning engine.

What is RAG? What is a semantic layer?

RAG (Retrieval-Augmented Generation) retrieves relevant documents or data from a knowledge base and feeds them into an LLM prompt so the model can ground its answer in your content. RAG consists of four primary components: a knowledge base (your documents or tables), a retriever that finds relevant documents, an integration layer that assembles retrieved context into a prompt, and a generator (the LLM) that produces the final response.
A semantic layer is a control layer that defines business relationships, metric logic, and authorized access patterns. In the context of AI agents, the semantic layer tells the system which tables join, which assets are authoritative, which metrics are defined consistently, and which users can access which data. The semantic layer translates business questions into correct, permission-aware queries.

Why retriever quality is critical

The retriever is the single biggest determinant of RAG output quality. Even the best LLM cannot compensate for poor retrieval. If a retriever returns 10 documents about "revenue" but they define revenue differently, the LLM must guess which definition is correct, or it returns a conflicting answer.
This is where the semantic layer becomes essential. The semantic layer reduces ambiguity by declaring relationships, organizing assets by business domain, and curating trusted definitions. When the retriever searches for "revenue," the semantic layer ensures it retrieves the authoritative definition, not conflicting definitions from other teams.

Key capabilities for semantic grounding

Capability	Why it matters
Relationship declarations (primary/foreign keys)	Agents join tables correctly instead of guessing.
Governed metric definitions (Metric Views)	Metrics defined once in code. All consumers use the same logic.
Domain organization	Group assets by business domain to scope retriever search.
Trusted asset curation (Pages)	Surface authoritative assets tied to each concept.
Permission-aware access	Row filters and column masks enforced at query time.
Usage-based authority ranking	Ontology ranks knowledge by authorship and usage.

How Databricks Genie Ontology bridges RAG and semantics

Genie Ontology is the unified context layer that makes RAG semantically aware. Genie Ontology combines two approaches:
Automatic context extraction. Genie Ontology continuously extracts knowledge from assets already connected to Databricks: notebooks, dashboards, SQL queries, connected apps. It builds a knowledge graph and injects relevant, permission-aware context into the agent at query time. This means you don't need to manually model everything upfront; the ontology learns from your existing assets.
Governed semantic grounding. Unity Catalog semantics (metric views, domains, and Pages) define business logic explicitly. Metric Views define measures and dimensions as governed code. Domains group assets into business-aligned collections. Pages capture shared business terms, listing the authoritative assets tied to each concept. When conflicts arise between inferred and governed context, the governed context takes precedence because it has been human-reviewed.
Together, automatic extraction and governed grounding create a hybrid model: the ontology learns from what you build, but you retain control over definitions that matter most.

Practical impact

Genie with ontology achieves higher accuracy and faster query resolution by reducing ambiguity. The semantic layer focuses the retriever on authoritative context instead of searching blindly.

Building the foundation

Define vocabulary, metric logic, relationships, and action boundaries:

1. Declare relationships. Tell the system how tables relate using primary/foreign keys. Agents join correctly.
2. Organize by domain. Group assets into Domains so the retriever focuses on relevant context.
3. Define governed metrics. Use Metric Views to define key metrics once in code so all downstream agents and dashboards use the same definition.
4. Curate trusted assets. Create Pages that surface the authoritative assets and definitions tied to each business concept.

The semantic layer transforms RAG from a generic document-retrieval system into an enterprise-aware reasoning engine by ensuring that every retrieved piece of context is grounded in business meaning, governed definitions, and permission-aware access.

FAQs

Does the semantic layer replace RAG?

No. The semantic layer enhances RAG by structuring the environment in which RAG operates. RAG retrieves context, and the semantic layer ensures that context is meaningful and aligned with how your business defines it. You need both.

Can I use Genie Ontology without Unity Catalog semantics?

Yes. Genie Ontology starts with automatic context extraction from your existing assets, so you can benefit from the ontology immediately. However, adding explicit semantic definitions (domains, metric views, Pages) improves accuracy and control over time.

How does the semantic layer handle conflicting definitions?

When the ontology encounters conflicting definitions (e.g., two teams define "revenue" differently), it ranks context by authority. Governed definitions (metric views, Pages) have higher authority than inferred context because they have been human-reviewed. You can audit and resolve conflicts in the Genie UI.

Can I control which assets the ontology uses to answer questions?

Yes. You can scope the ontology to specific domains, hide assets from the ontology, or mark assets as "trusted" to increase their authority. This fine-tuning ensures the ontology uses the right context for your use case.

Is Genie Ontology available now?

Genie Ontology is in preview. Reach out to your Databricks team for access and guidance on building a semantic layer for your organization.