RAG in one sentence
Retrieval-augmented generation finds material relevant to a question and supplies it to a language model before the model answers. It is effective when the needed evidence is contained in a manageable set of documents or chunks.
RAG improves access to enterprise knowledge, but semantic similarity does not guarantee that the retrieved material uses consistent definitions or captures all relevant relationships.
What GraphRAG adds
GraphRAG uses a graph representation of entities, relationships and often communities or summaries to guide retrieval. It can follow connections that are difficult to discover through passage similarity alone, such as ownership, dependency, lineage, exposure and influence.
The graph may be built from documents, structured data or both. Its value depends on entity resolution, relationship quality, provenance and the semantic model used to interpret the connections.
When GraphRAG is the stronger choice
GraphRAG is valuable when questions require multi-hop reasoning, impact analysis, relationship discovery, cross-document synthesis or a complete view of a connected subject. Examples include tracing which reports and AI agents depend on a changed data contract or identifying how a supplier event affects products, facilities and commitments.
It is not automatically better for every search. Straightforward policy lookup, document summarization and narrow question answering may be faster and less expensive with conventional RAG.
Why ontology still matters
A graph can connect nodes without establishing whether those connections preserve enterprise meaning. Ontology defines the concepts and relationship semantics that make traversal interpretable. It also helps reconcile aliases, roles, types and contextual definitions.
GraphRAG without semantic governance can scale connected ambiguity. Ontology without operational data remains abstract. Used together, they connect governed meaning to evidence.
A pragmatic architecture
Use hybrid retrieval. Apply vector search for semantic recall, graph traversal for connected context, structured queries for precise facts and reranking for task relevance. Preserve citations and source authority across every path.
The decision should be driven by the question shape, not by fashion. Add graph complexity when relationships materially improve completeness, explanation or control.
RAG retrieves related content. GraphRAG retrieves connected context. Ontology determines whether that context means what the enterprise thinks it means.