RAG vs. GraphRAG: Choosing the Right Approach for Your Documents

Retrieval-Augmented Generation (RAG) has become the standard approach for giving large language models access to external knowledge. But recently, GraphRAG has emerged as an alternative, leveraging knowledge graphs for context retrieval. Which should you use? The answer, as always: it depends.

Understanding RAG

How It Works

Traditional RAG follows a straightforward pipeline:

1. Chunking: Break documents into manageable segments
2. Embedding: Convert chunks to vector representations
3. Indexing: Store embeddings in a vector database
4. Retrieval: Find semantically similar chunks for a query
5. Generation: Provide retrieved chunks as context to the LLM

Strengths

Simplicity: Well-understood pipeline with mature tools Speed: Vector similarity search is highly optimized Citation: Easy to trace responses back to source documents Incremental Updates: Add new documents without reprocessing everything Cost: Lower computational overhead than graph construction

Limitations

Lost Relationships: Chunking can break apart related information No Cross-Document Understanding: Struggles with synthesis across sources Keyword Dependence: May miss relevant content with different terminology Context Boundaries: Chunk boundaries can split critical context

Best Use Cases

• Document Q&A with clear, separable content
• Citation and audit trail requirements
• Frequently updated document sets
• Cost-sensitive applications
• Documents with distinct sections

Understanding GraphRAG

How It Works

GraphRAG takes a different approach:

1. Entity Extraction: Identify key entities across documents
2. Relationship Mapping: Extract connections between entities
3. Graph Construction: Build knowledge graph representation
4. Query Processing: Translate queries to graph traversal
5. Context Assembly: Gather graph-based context for LLM

Strengths

Relationship Preservation: Maintains connections between concepts Cross-Document Synthesis: Naturally handles multi-document reasoning Semantic Richness: Captures entity types and relationship semantics Inference: Enables reasoning beyond explicit statements Holistic Understanding: Provides structural document knowledge

Limitations

Complexity: More sophisticated infrastructure required Upfront Cost: Graph construction is computationally expensive Update Overhead: Adding documents may require graph restructuring Query Complexity: Requires more sophisticated query processing Entity Ambiguity: Same names may refer to different entities

Best Use Cases

• Documents with extensive cross-references
• Multi-document analysis and synthesis
• Relationship-heavy queries (“How are X and Y connected?”)
• Domains with clear entity types (legal, technical, medical)
• Long-term document sets with stable structure

The Comparison

Aspect	RAG	GraphRAG
Setup Complexity	Low	High
Query Speed	Fast	Moderate
Cross-Document	Limited	Excellent
Relationships	Weak	Strong
Incremental Updates	Easy	Moderate
Citations	Straightforward	Complex
Cost (Initial)	Low	High
Cost (Query)	Low	Moderate

Real-World Scenarios

Scenario 1: Legal Due Diligence

Documents: 500 contracts, 2,000 exhibits, 5,000 disclosure documents

Queries:

• “What are the indemnification terms?” (RAG-friendly)
• “Which contracts reference Company X and what are their relationships?” (GraphRAG-friendly)

Recommendation: Hybrid Approach

• RAG for clause retrieval and citation
• GraphRAG for entity relationships and cross-document analysis

Scenario 2: Construction Specifications

Documents: 15 specification volumes, 1,000 submittals, 500 RFIs

Queries:

• “What are the concrete strength requirements?” (RAG-friendly)
• “How do window specifications relate to energy code requirements?” (GraphRAG-friendly)

Recommendation: RAG-First with Graph Enhancement

• RAG for most specification lookups
• Graph layer for cross-spec relationships

Scenario 3: Systems Engineering

Documents: 50 subsystem specs, 200 interface control documents, 1,000 requirements

Queries:

• “What are the power requirements for subsystem A?” (RAG-friendly)
• “How does a change in subsystem B affect subsystem C?” (GraphRAG-friendly)

Recommendation: GraphRAG-Primary

• Requirements traceability demands relationship understanding
• Interface management is inherently graph-like
• RAG for detailed requirement text retrieval

Hybrid Approaches: The Best of Both

In practice, many successful implementations combine RAG and GraphRAG:

RAG + Graph Enrichment

Use RAG for fast retrieval, but enhance chunks with graph-based relationship information.

Graph-Guided RAG

Use graph structure to improve RAG retrieval—find relevant chunks by graph proximity.

Hierarchical Approach

GraphRAG for high-level document navigation, RAG for detailed content retrieval.

Query-Dependent Routing

Analyze query type and route to RAG, GraphRAG, or both based on question structure.

Decision Framework

Choose RAG when:

• Documents have clear, separable sections
• Citations and audit trails are critical
• Budget is constrained
• Fast setup is needed
• Documents change frequently

Choose GraphRAG when:

• Document relationships are complex
• Cross-document reasoning is required
• Entity-centric queries are common
• Long-term investment is feasible
• Relationship understanding drives value

Choose Hybrid when:

• You need both citation and relationship understanding
• Document set is large and complex
• Query types vary significantly
• Budget allows sophisticated implementation
• Maximum flexibility is required

Conclusion

RAG and GraphRAG aren’t competitors—they’re complementary techniques with different strengths. The best solution often involves both, strategically applied based on document characteristics and query patterns.

At TeraContext.AI, we analyze your specific needs and implement the optimal combination of techniques for your use case. Because the goal isn’t to use the latest technology—it’s to solve your problem effectively.

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Unsure which technique fits your docs? Contact us to compare RAG/GraphRAG for your use case.