RAG vs Knowledge Graphs for Customer Support
Retrieval-augmented generation finds similar text. Knowledge graphs encode structure. Combine them for accurate support replies.
RAG and knowledge graphs are not competitors. They solve different parts of the same problem.
RAG is excellent at fetching passages that look semantically close to a question. Knowledge graphs are excellent at encoding the structural truth of a product. Pricing tiers, feature gates, plan upgrades, error codes, dependency chains.
Where each falls short alone
RAG alone often retrieves three plausible passages that contradict each other. The model picks one and sounds confident. The customer gets a wrong answer.
A graph alone cannot capture the soft language of how a feature is described in a help article. It can say two nodes are related, but not how a human would explain the relationship.
What works in production
Combine both. Use the graph to constrain retrieval to the right region of the product. Use RAG to surface the human language that explains it. Cite the sources. Score the confidence. Let a human approve the reply.
Building support AI you can actually trust?
SupportGraph turns docs and ticket history into a product knowledge graph for source-backed AI replies. Early access is open.