Evaluating Graph-RAG vs. Standard RAG: A Hallucination Benchmark on Fact-Dense Queries

The authors present a deterministic 3‑Tiered Graph‑RAG architecture that outperforms a conventional vector‑based RAG pipeline on fact‑dense queries, reporting a 35 % reduction in hallucination rates as measured by the FactCheck‑Metric. The Graph‑RAG system leverages a hierarchical knowledge graph for retrieval, a rule‑based fusion layer, and a lightweight language model for final generation, achieving a 12 ms per‑query latency on a single A100 GPU. While the approach improves factual fidelity, it requires a pre‑built graph index and incurs higher storage overhead compared to flat vector indices. The benchmark demonstrates that Graph‑RAG can be a viable alternative when accuracy outweighs index‑building cost.

⚡ Key Takeaways

• 35 % lower hallucination rate on fact‑dense queries versus standard vector RAG.
• Uses a deterministic 3‑Tiered retrieval: graph‑based, rule‑based fusion, then language model generation.
• Latency of 12 ms per query on a single A100 GPU, lower than the 18 ms typical for vector RAG.
• Requires a pre‑built knowledge‑graph index, increasing storage demands by ~40 % over flat vectors.
• WhyItMatters: Engineers building production systems that prioritize factual correctness—such as knowledge‑base chatbots or compliance‑aware assistants—can reduce hallucinations without sacrificing latency by adopting a Graph‑RAG pipeline.
• TechnicalLevel: Intermediate
• TargetAudience: ML Engineers, Retrieval Engineers
• PracticalSteps:
• Run the benchmark script with `--mode graph-rag` to generate hallucination statistics.
• Compare the output against the standard RAG baseline using the provided FactCheck‑Metric scorer.
• Tune the graph‑retrieval tier thresholds in the `config.yaml` to balance index size and accuracy.
• ToolsMentioned: Graph‑RAG, vector RAG, FactCheck‑Metric
• Tags: RAG, DEPLOYMENT, INFERENCE, PYTHON

🔧 Tools & Libraries

Engineers building production systems that prioritize factual correctness—such as knowledge‑base chatbots or compliance‑aware assistants—can reduce hallucinations without sacrificing latency by adopting a Graph‑RAG pipeline.

✅ Practical Steps

1. Run the benchmark script with `--mode graph-rag` to generate hallucination statistics.
2. Compare the output against the standard RAG baseline using the provided FactCheck‑Metric scorer.
3. Tune the graph‑retrieval tier thresholds in the `config.yaml` to balance index size and accuracy.

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