Intent Graph vs RAG: What's the Difference?
RAG retrieves text chunks by similarity. The Intent Graph stores typed facts by explicit structure. When you need to know a customer's environment, you need "hospital" — not three paragraphs that mention hospitals. RAG answers "what content is relevant to this query?" The Intent Graph answers "what specific facts do we know about this customer?" Similarity is not understanding. Typed facts are queryable by explicit criteria, not similarity thresholds.
Comparison Table
RAG retrieves documents for knowledge queries. The Intent Graph stores customer facts for personalization. Use RAG to ground AI responses in your content. Use the Intent Graph to carry customer context across touchpoints.
| Dimension | RAG | Intent Graph |
|---|---|---|
| Storage model | Text chunks (embeddings) | Typed facts (structured) |
| Query method | Similarity search | Explicit field queries |
| Returns | Relevant passages | Exact values |
| Use case | Knowledge retrieval | Customer context |
| State | Stateless (per query) | Stateful (accumulates) |
Why This Distinction Matters
RAG is essential for grounding AI responses in your content. When someone asks "What's the warranty on the X500?", RAG retrieves the relevant documentation and generates an accurate answer.
But RAG only sees the current query. If that customer spent 20 minutes configuring a system with specific requirements, chatted about integration constraints, and then asks about warranty — a RAG system doesn't know any of that context. It just retrieves warranty docs.
The Intent Graph provides the missing layer. It remembers that this customer needs the X500 for a high-temperature application with specific pressure requirements. When they ask about warranty, the system doesn't just retrieve generic terms — it surfaces the conditions specifically relevant to their use case.