GraphRAG - Is it worth It? (Absolutely!)

Putting GraphRAG to test, knowing it’s worth and when to deploy it!

The battle between knowledge graphs and vector databases just got real. Here’s what 160 queries and rigorous evaluation revealed about the future of AI-powered search.

The Information Retrieval Revolution We Didn’t See Coming

Imagine asking an AI system: “What are the connections between reinforcement learning and robotics?”

A traditional RAG system might give you a decent answer based on document similarity. But what if that same system could understand that DeepMind’s research team led by David Silver worked on both AlphaGo (reinforcement learning) and later collaborated with Boston Dynamics on robotic applications, creating a web of knowledge that goes far beyond simple document matching?

That’s the promise of GraphRAG—and after putting it through the most rigorous evaluation I’ve ever conducted, I can tell you: We can’t ignore GraphRAG.

What Is GraphRAG, Really?

Before diving into the data, let’s establish what we’re talking about. Traditional RAG (Retrieval-Augmented Generation) works like this:

Document Chunking: Break documents into pieces
Vector Embedding: Convert text to numerical representations
Similarity Search: Find chunks most similar to your query
Generation: Feed retrieved chunks to an LLM for synthesis

GraphRAG takes this foundation and adds a crucial layer:

Knowledge Graph Construction: Extract entities, relationships, and concepts (although Microsoft’s original paper proposes to use LLMs, I wonder, why can’t we just use simple NER?!)
Entity Linking: Connect related entities across documents
Graph-Enhanced Retrieval: Use both vector similarity AND graph relationships
Multi-hop Reasoning: Traverse connections to find non-obvious insights (This is what we need to test!!)

Think of it as the difference between a library catalog (traditional RAG) and a research assistant who knows how every book, author, and concept connects to every other (GraphRAG).

P.S. Got this analogy from LLM (Gemini Pro 2.5)

The Experiment: 160 Queries, Zero Bias

We use LLM as a judge and it has the context of only query and the summary LLM generated

The Dataset: 1,000+ documents from diverse sources

550+ research papers from ArXiv and Semantic Scholar
250+ tech news articles from TechCrunch, VentureBeat, Wired
200+ GitHub repositories with AI/ML focus
500+ entities in our knowledge graph with rich interconnections

The Evaluation Framework

160 carefully crafted queries across 8 categories
Blind LLM judge evaluation (Claude 3.5 Sonnet)
6 evaluation criteria: Completeness, Accuracy, Contextual Depth, Clarity, Relevance, Actionable Insights

Note: I added this to improve the data-set and queries. I used O3-mini for first 10-15 trials and then switched to Claude 3.5 as a judge

The Categories Tested

AI/ML Research - “Latest advances in transformer architectures”
Technical Deep Dive - “How does gradient descent optimization work?”
Industry Applications - “How are companies using federated learning?”
Comparative Analysis - “GraphRAG vs traditional RAG differences”
Future Directions - “What’s next for multimodal AI?”
Company Technology - “What AI research is Google focusing on?”
Cross Domain Connections - “Relationship between NLP and computer vision”
Research Trends - “Who are the key researchers in reinforcement learning?”

Decoding the Results

My initial thought was to use — (em-dash) instead of : (colon) in the above heading; side effects of using LLM too much!

These aren’t marginal improvements. These seemed consistent and assertive (at least for the experiment setup we did!)

Where GraphRAG Absolutely Dominates

The category breakdown reveals where GraphRAG excels:

The Criteria That Matter Most

Breaking down performance by evaluation criteria:

P.S. Note how traditional RAG always scores 7.5 (I felt it is a code bug somehow the summary of RAG systems always averaged out to 7.5. Will be triggering a few more tests to understand this behavior. Expect a part 2 of the article!)

Notice something interesting? GraphRAG doesn’t just win on technical metrics—it provides more relevant, actionable, and complete answers. This stems from the number of documents it was able to retrieve.

Judge Confidence: The Trust Factor

Perhaps most importantly, the AI judge was confident in its decisions:

45.6% of decisions made with high confidence (>80%)
54.4% of decisions made with medium confidence (60-80%)
0% low confidence decisions
Average confidence: 78.2%

This isn’t just statistical noise—these are clear, reliable quality differences.

Sample Query and The Difference

Query: “What researchers are working on BERT and transformer models?”

Traditional RAG Response:

“BERT was developed by Google AI researchers including Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. The transformer architecture was introduced by Vaswani et al. in ‘Attention Is All You Need’…”

GraphRAG Response:

“The BERT and transformer research landscape involves several key researchers and institutions:

Google AI Team: Jacob Devlin (lead BERT researcher), Ming-Wei Chang, Kenton Lee, and Kristina Toutanova developed BERT. They built on transformer work by Ashish Vaswani, Noam Shazeer, and others.

Academic Collaborations: The original transformer paper came from Google Brain and Google Research, with contributions from University of Toronto (Aidan Gomez) and others.

Follow-up Research: Tom Brown (OpenAI) led GPT development, while Luke Zettlemoyer (University of Washington/Meta) worked on RoBERTa improvements.

Current Developments: Researchers like Colin Raffel (UNC/Google) are exploring T5 and unified text-to-text transformers, while Yann LeCun’s team at Meta investigates self-supervised learning applications…”

The Difference: GraphRAG understands that researchers move between institutions, collaborate across organizations, and that understanding these relationships provides much richer context.

Performance Considerations

Query Latency: GraphRAG adds ~200ms overhead for graph traversal
Storage: Knowledge graph requires ~2GB for 1000 documents (I could have optimized this with a few code tweaks)
Accuracy: 7.62/10 vs 7.5/10 (minimal difference in factual accuracy)
Completeness: 8.46/10 vs 7.5/10 (significant improvement in comprehensiveness)

When GraphRAG Fails (And Why That Matters)

These are some limitations I have identified (not comprehensive though!)

Where Traditional RAG Still Competes

1. Simple Factual Queries

Example: “What is the capital of France?”

Traditional RAG: Fast, direct, accurate
GraphRAG: Overkill with similar results

2. Highly Technical Deep Dives

Example: “Explain back propagation mathematics”

Traditional RAG: Focused, detailed technical content
GraphRAG: May add unnecessary context

3. Single-Document Answers

When: The answer lives in one specific document

Traditional RAG: Efficient document retrieval
GraphRAG: Graph overhead without benefit

The Trade-offs

The Future of RAG: What’s Next?

Based on these results, here’s where I see the field heading:

Hybrid Approaches Will Dominate

The future isn’t GraphRAG vs Traditional RAG—it’s intelligent routing:

Simple queries → Traditional RAG
Complex relationship queries → GraphRAG
Mixed complexity → Hybrid approach

Dynamic Knowledge Graph Construction

Current GraphRAG requires manual graph construction. Next-generation systems will:

Auto-generate knowledge graphs from documents
Update graphs in real-time as new information arrives
Learn optimal graph structures for specific domains

Specialized Domain Graphs

The Bottom Line

GraphRAG isn’t just an incremental improvement—it’s a paradigm shift toward AI systems that understand relationships and context the way humans do. The 36% performance advantage we measured is just the beginning. (Results can vary based on data-set but consider it a win for this data-set at least!)

As knowledge graphs become easier to construct and maintain, and as AI systems become better at understanding relationships, GraphRAG will become the standard for any application requiring deep, contextual understanding.

The question isn’t whether GraphRAG is better than traditional RAG—our data proves it is for complex queries. The question is: Are you ready to implement it?

The Full Code, Dataset, Queries and Evaluations are available here in the GitHub repository

The future of information retrieval is relationship-aware, contextually rich, and powered by knowledge graphs. The question is: when will you make the jump?

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