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GraphRAG vs RAG: How a Knowledge Graph Cut Token Usage by 90% While Hitting 100% Accuracy

Author: Muthukumaran
Published:
Source: https://medium.com/@muthukumaran.42510/graphrag-vs-rag-how-a-knowledge-graph-cut-token-usage-by-90-while-hitting-100-accuracy-a5c11ba35452
Fetched: 2026-06-06T23:56:48.941238

GraphRAG vs RAG: How a Knowledge Graph Cut Token Usage by 90% While Hitting 100% Accuracy

This was built for the GraphRAG Inference Hackathon by TigerGraph, where we went head-to-head against teams globally to prove graphs beat vector search on every metric that matters

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TL;DR: We built a 3-pipeline benchmarking system on TigerGraph GraphRAG. GraphRAG uses 90% fewer tokens than Basic RAG, achieves 100% LLM-as-a-Judge pass rate, and scores 0.97 BERTScore — hitting every bonus threshold. Scaled to a 108-million-token Wikipedia corpus in Round 2. Here’s exactly how we did it.

The Problem

LLMs burn thousands of tokens per query. Basic RAG (vector search + LLM) helps, but it retrieves similar chunks — not connected knowledge. A question like “Which physicist’s work led to GPS time corrections?” requires reasoning across multiple entities. Vector search returns paragraphs. Graphs return answers.

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What We Built

A 3-pipeline system that runs every query through three approaches simultaneously:

  • Pipeline 1 — LLM-Only: No retrieval. Baseline. ~142 tokens/query.
  • Pipeline 2 — Basic RAG: Vector embeddings + top-K chunks. ~2,799 tokens/query.
  • Pipeline 3 — GraphRAG: TigerGraph entity traversal + compact KG context. ~382 tokens/query.

Built on the official TigerGraph GraphRAG repo, with 14 novel techniques added on top.

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The Architecture (4 Layers)

Layer 4: Evaluation    — Groq Llama-3.3-70B judge · BERTScore · F1/EM  
Layer 3: LLM           — 12 providers (Gemini, Claude, GPT-4, Groq, Mistral...)  
Layer 2: Orchestration — Adaptive Router · 3-Pipeline Manager · NoveltyEngine  
Layer 1: Graph         — TigerGraph Cloud · GSQL traversal · Entity/Relation store

Round 2: Scaling to 108 Million Tokens

Round 1 ran on 2.5M tokens. For Round 2 we scaled to a 108,874,614-token Wikipedia science corpus — 94,932 articles, ~850K chunks — verified with Gemini’s count_tokens API. TigerGraph Savanna's native graph traversal kept query latency flat as the corpus grew 40×.

Three GraphRAG Pain Points We Solved

Pain Point 1 — Sibling chunk loss: Standard GraphRAG retrieves a single top-K chunk. If context spans the next paragraph, it’s lost. Fix: getDocumentChunks GSQL query fetches all chunks from the same document, ordered by chunk_index.

Pain Point 2 — Entity relationship blindness: Vector search can’t traverse relationships. Fix: entityHopChunks GSQL — hops Chunk → MENTIONS → Entity → RELATED_TO → Entity → back to Chunks, surfacing thematically linked content the query never touched.

Pain Point 3 — Empty entity-hop fallback: If entity-hop returns nothing (sparse graph), context is empty. Fix: regex-extract capitalized entity names, embed them, and fall back to vector search on the entities — not the raw query.

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Benchmark Results

10 Wikipedia science questions. Gemini 2.5 Flash. Independent judge: Groq Llama-3.3–70B.

Pipeline | Tokens/Query | Cost/Query | LLM-Judge | BERTScore
LLM-Only | 142 | $0.000014| 100% | —
Basic RAG | 2799| $0.000611| 100% | —
GraphRAG | 382| $0.000043 | 100% | 0.9726
GraphRAG vs | −90% | −93% | BONUS ✅ | BONUS ✅

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The Token Story

GraphRAG’s entity descriptions are pre-indexed at ingest time. At query time, instead of sending 4,418 tokens of raw chunk text, we send 448 tokens of structured entity context: “General relativity: geometric theory of gravitation published by Albert Einstein in 1915.” Same knowledge, 90% fewer tokens, zero accuracy loss.

At 10M queries/month, that’s ~$5.7M/month saved vs Basic RAG. The graph index is paid once at ingest — savings compound per query.

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14 Novel Techniques

We layered 6 research-backed novelties on top of the base TigerGraph GraphRAG:

  1. PPR Confidence Retrieval (CatRAG) — +2.9% F1 in ablation
  2. Spreading Activation (SA-RAG) — +1.8% F1
  3. Flow-Pruned Paths (PathRAG) — bridge question accuracy
  4. Token Budget Controller (TERAG) — enforces token ceiling per query
  5. PolyG Hybrid Router (RAGRouter-Bench) — +2.1% F1
  6. Incremental Graph Updates (TG-RAG) — 92% faster re-ingestion

Try It Live

Bring your own API key — enter it directly in the UI. No server-side key storage.

Built for the GraphRAG Inference Hackathon by TigerGraph · #GraphRAGInferenceHackathon #TigerGraph #GraphRag