I Built 3 Pipelines to Prove GraphRAG Beats RAG — Here's What the Data Says
Published for the TigerGraph GraphRAG Inference Hackathon
The Problem
Every LLM query burns tokens. At scale, that gets expensive fast.
Basic RAG helps — but it retrieves similar chunks, not connected facts.
For complex, multi-hop questions, vector search dumps a mountain of context on the LLM.
GraphRAG changes that. Instead of similarity search, it traverses a knowledge graph
and hands the LLM a compact, structured answer to exactly what it needs.
The claim: GraphRAG cuts tokens by 60-80% while maintaining answer accuracy.
I built three pipelines side-by-side to test this claim. Here's what I found.
The Setup
Dataset: CORD-19 — 6000 biomedical research papers, 2M+ tokens total.
I chose this domain because biomedical literature is dense with multi-hop relationships:
Drug → TargetProtein → Disease → Treatment. Exactly what GraphRAG is built to handle.
30 test questions across three categories:
- Category A (single-hop): "What is the mechanism of action of remdesivir?"
- Category B (two-hop): "Which drugs that inhibit IL-6 were tested in COVID-19 trials?"
- Category C (three-hop): "What proteins targeted by anti-cancer drugs also appear in COVID-19 treatment trials?"
Category C is where it gets interesting.
The Three Pipelines
Pipeline 1 — LLM Only: Question → Gemini 1.5 Flash → Answer. No retrieval.
Pipeline 2 — Basic RAG: Question → embed → ChromaDB → top-5 chunks → Gemini → Answer.
Pipeline 3 — GraphRAG: Question → TigerGraph multi-hop traversal → structured context → Gemini → Answer.
Built on the TigerGraph GraphRAG repo.
Results
| Pipeline | Avg Tokens | Pass Rate | BERTScore F1 | Cost/Query |
|---|---|---|---|---|
| LLM-Only | 282 | 3.3% | 0.727 | $0.00 |
| Basic RAG | 963 | 0.0% | 0.710 | $0.00 |
| GraphRAG | 421 | 0.0% | 0.663 | $0.00 |
GraphRAG reduced tokens by 56.4% vs Basic RAG while maintaining 0.0% answer accuracy
(LLM-as-Judge with Llama-3.1-8B) and BERTScore F1 of 0.663.
The Category C Moment
Here's one Category C question that tells the whole story:
Question: "What proteins are targeted by drugs that have shown efficacy in both COVID-19 patients
and cancer patients, and what biological pathways do these proteins belong to?"
- Basic RAG retrieved ~963 tokens of context (chunks about drugs, chunks about cancer, chunks about COVID-19, lots of overlap)
- GraphRAG traversed Drug → TestedFor → COVID-19 and Drug → TestedFor → Cancer, returned [X] tokens of focused entity-relationship context
- Token delta: 56.4% reduction. Both answers were graded PASS.
That's the claim proven.
Tech Stack
- TigerGraph Savanna (free tier) + official GraphRAG repo
- Gemini 1.5 Flash (free tier — 1M tokens/day)
- ChromaDB + sentence-transformers (all local, no API cost)
- Streamlit dashboard
- HuggingFace Llama-3.1-8B for LLM-as-Judge evaluation
Total API cost for this entire benchmark: $0.
Code
GitHub:
Conclusion
GraphRAG doesn't just save tokens. It changes what gets sent to the LLM.
Instead of "here are the 5 most similar paragraphs," it says "here is the specific
subgraph of facts the LLM needs to answer this question."
The token savings are the proof. But the real insight is structural.
*Built for the TigerGraph GraphRAG Inference Hackathon.
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