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GraphRAG Builder Playbook

How to build GraphRAG that holds up in production, based on 300+ enterprise AI builds

Most GraphRAG demos work. Far fewer survive real data, real users and a real token budget. This playbook draws on more than 300 enterprise AI applications built on TigerGraph, where teams compared GraphRAG with basic RAG on answer accuracy and token use. It turns what the strongest builds had in common into a practical guide you can apply to your own data.

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Key findings from 300+ GraphRAG builds

Fewer tokens per answer

On average, builds used 80.7% fewer tokens than basic RAG, and up to 94.6% in some applications, because the model received connected facts instead of long text chunks.

Hybrid retrieval wins on hard questions

Pairing vector search with graph traversal answered multi-hop questions that similarity search alone missed.

The model of your data matters more than the prompt

Teams that defined entities and relationships first spent less time tuning prompts later.

Every answer can be traced

Graph retrieval shows the exact entities and links behind each response, which makes answers easier to test, debug and explain.

Basic RAG is still fine for simple lookups

GraphRAG pays off when the answer depends on how facts connect.

What the playbook covers

How to model entities and relationships from documents and structured data

When to use vector, graph or hybrid retrieval for each type of question

How to keep token cost under control as the graph grows

How to make answers explainable with traceable retrieval paths

A production architecture for ingestion, live updates, latency and evaluation

How to measure accuracy and ROI against a basic RAG baseline

GraphRAG questions, answered

What is GraphRAG?

GraphRAG is retrieval-augmented generation that pulls context from a knowledge graph, not only from text chunks. The model receives entities and the relationships between them, so it can answer questions that depend on how facts connect rather than on which passages look similar.

What is hybrid search in RAG?

Hybrid search combines vector similarity search with another retrieval method in one query. In GraphRAG, vector search finds the most relevant starting points and graph traversal then follows their relationships, so the model gets both semantically similar content and the connected facts around it.

How do you build a GraphRAG application?

Start by modeling the entities and relationships that matter, then load your data into a graph and add vector embeddings for hybrid search. Route each question to graph, vector or both, pass the connected context to the LLM, and test accuracy and token use against a basic RAG baseline.

Can a graph database replace a vector database?

For many GraphRAG builds it can, if the graph database also supports vector search. TigerGraph runs graph traversal and vector search together in a single query, so teams can keep embeddings and relationships in one system instead of syncing a separate vector store.

Build GraphRAG that works beyond the demo

Get the full playbook, with the architecture patterns and evaluation methods from 300+ builds. Want to go further? See it on your data with a TigerGraph demo or try it free in Savanna.

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