If you’re building AI applications with Retrieval-Augmented Generation, you’ve probably hit the wall. The one where your vector search returns five semantically similar chunks, your LLM confidently stitches them together, and the answer is still wrong.

It’s not a hallucination problem. It’s a retrieval architecture problem.

Traditional vector RAG—embedding text chunks and retrieving by semantic similarity—works beautifully for a wide range of use cases. But there’s an entire class of questions it fundamentally cannot answer well, no matter how you tune your embeddings or chunk size. These are questions that require understanding relationships between things, not just finding things that sound similar.

That’s the gap GraphRAG fills. And the benchmarks are starting to quantify just how significant that gap is.

What Actually Makes GraphRAG Different (Beyond the Buzzwords)

The distinction comes down to how your system represents knowledge before the LLM ever sees it.

Vector RAG converts your documents into numerical representations (embeddings) and stores them in a vector database. When a query arrives, it gets embedded the same way, and the system retrieves whichever chunks land closest in that mathematical space. It’s fast, scalable, and remarkably good at finding content that’s about the same topic as your question.

GraphRAG represents your knowledge as a structured network—entities (people, products, concepts) connected by typed relationships (reports_to, caused_by, contains). Instead of searching by similarity, it traverses these connections to assemble precise, relationship-aware context for the LLM.

The difference matters most when the answer to a question isn’t sitting in any single chunk of text. It matters when the answer lives in the connections between chunks.

It’s worth noting that Gartner recently designated knowledge graphs as a “Critical Enabler” with immediate impact on GenAI—a signal that the industry is moving past the experimentation phase.

The Accuracy Gap: What the Data Shows

Before walking through specific scenarios, it helps to understand the scale of the performance difference.

In Fluree’s research on RAG accuracy, we compared three retrieval architectures across enterprise question-answering tasks:

• Centralized relational data (traditional RAG): Zero-shot accuracy started around 20%, improving to roughly 80% with extensive data integration and model fine-tuning.
• Centralized knowledge graphs (GraphRAG): Zero-shot accuracy jumped to 60-65%, reaching up to 95% with fine-tuning and enriched data.
• Decentralized knowledge graphs: Consistently hit 90-99% accuracy by connecting data sources across domains without physically centralizing them.

The takeaway isn’t just that GraphRAG is more accurate—it’s that the accuracy gap widens as queries become more complex and span more data sources. That pattern shows up clearly in the seven scenarios below.

Seven Scenarios Where GraphRAG Consistently Outperforms Vector Search

1. Multi-Hop Reasoning Across Scattered Information

The failure mode: Vector search retrieves semantically similar chunks independently. When an answer requires connecting information from multiple documents—following a chain of relationships—it retrieves the pieces but misses the links between them.

What this looks like in practice: You query an enterprise knowledge base: “What projects has Alice worked on with people who reported to Bob?”

Vector RAG might surface Alice’s project history and Bob’s org chart separately. But the system has no mechanism to traverse the actual chain: Alice → workedOn → Project ← workedOn → Person → reportsTo → Bob. Without that traversal, the LLM is left guessing at connections—and guessing is where hallucinations start.

GraphRAG follows the relationship chain explicitly, returning only the projects where the connection actually exists in your data.

This is the scenario where the accuracy gap is most dramatic. Research consistently shows that vector RAG accuracy degrades toward zero as the number of entities per query increases beyond five, while graph-based retrieval maintains stable performance even with 10+ entities.

2. Navigating Organizational and Hierarchical Structures

The failure mode: When you flatten a hierarchy into text chunks, you lose the structural relationships that business queries depend on. Vector search has no concept of “parent” or “child” in an org chart.

What this looks like in practice: “What policies affect the Supply Chain department?”

Vector RAG retrieves documents mentioning “Supply Chain.” But it misses inherited policies from parent departments—policies that apply to Operations, which Supply Chain sits under, or company-wide policies that cascade down. These are critical to a complete answer, and they may never mention “Supply Chain” at all.

GraphRAG traverses the hierarchy: Supply Chain → partOf → Operations → partOf → Company, collecting applicable policies at each level. Nothing falls through the cracks because the structure is explicit.

This is particularly relevant for regulated industries—banking, healthcare, defense—where policy inheritance isn’t optional, it’s a compliance requirement.

3. Building a Complete Picture of Any Entity

The failure mode: When the same entity appears across dozens of documents, vector search returns whichever chunks happen to score highest on similarity. You get fragments, not a unified view.

What this looks like in practice: “Tell me everything about Product X across our documentation.”

Vector RAG returns the top-k chunks mentioning Product X—maybe a feature description, a pricing page, and a support ticket. But it misses the customer implementations, the competitive positioning doc, the engineering roadmap, and the three incident reports from Q3.

GraphRAG retrieves the Product X node and traverses all its relationships: features, version history, customer deployments, open issues, competitive landscape, pricing changes. The difference between fragments and a complete picture is the difference between a useful answer and a misleading one.

4. Preserving Temporal and Causal Chains

The failure mode: Time sequences and cause-effect relationships are implicit in text. Vector embeddings capture topical similarity, not chronological or causal order.

What this looks like in practice: “What events led to the Q3 security breach?”

Vector RAG retrieves incident reports that mention the breach. But it can’t sequence them or establish causation. Did the phishing email cause the credential theft, or was it the other way around? The chunks don’t say—at least not in a way the retrieval system can reason about.

GraphRAG follows explicit causedBy and precededBy edges: Phishing Email → Credential Theft → Lateral Movement → Data Exfiltration. The causal chain is traceable and auditable, not inferred.

This auditability matters beyond just getting the right answer. In compliance-sensitive environments, you need to trace every piece of data back to its origin and review its history over time. That’s a problem that requires the retrieval layer itself to be trustworthy—not just the generation layer.

5. Enabling Exploratory and Gap-Analysis Queries

The failure mode: Vector search requires you to know roughly what you’re looking for. It’s poor at discovery, pattern detection, and “show me what I’m missing” queries.

What this looks like in practice: “What expertise gaps exist in our AI team based on current project requirements?”

This is nearly impossible with vector RAG because there’s no single chunk that contains the answer. The answer emerges from comparing two sets of relationships: Project → requires → Skill versus TeamMember → has → Skill. GraphRAG can compute the difference and surface the gaps.

These analytical, exploratory queries are increasingly common as organizations use RAG for strategic decision-making, not just Q&A.

6. Reducing Hallucination with Structured Context

The failure mode: When an LLM receives multiple retrieved chunks, it can fabricate connections between them. This is one of the most dangerous failure modes because the answers sound authoritative.

What this looks like in practice: Your retrieved context mentions both “Tesla” and “SpaceX.” A traditional RAG system might lead the LLM to infer a direct business relationship between the two companies—they share a founder, after all.

GraphRAG shows the actual structure: Elon Musk → founded → Tesla, Elon Musk → founded → SpaceX, with no direct company-to-company edge. The absence of a relationship is as informative as its presence.

By expressing data and metadata semantically, LLMs receive structured facts rather than ambiguous text—and structured facts are far harder to hallucinate around. This is a core reason why knowledge graph-based RAG consistently outperforms vector-only approaches in accuracy benchmarks: the context itself is more precise.

7. Disambiguating Entities with the Same Name

The failure mode: “Apple,” “Mercury,” “Jordan”—vector embeddings blend multiple meanings into a single region of embedding space, making disambiguation unreliable.

What this looks like in practice: A query about “Apple’s supply chain” should return information about the technology company. But vector RAG might retrieve content about fruit agriculture because both domains use supply chain terminology.

GraphRAG maintains distinct entities: Apple_(company) with edges like isA → Technology_Company → manufactures → iPhone versus Apple_(fruit) with edges like isA → Produce → grownIn → Orchards. The typed relationships eliminate ambiguity before the LLM ever generates a response.

When Vector RAG Is Still the Right Choice

GraphRAG isn’t universally better—it’s better for a specific class of problems. Stick with traditional vector RAG when:

• Your queries are straightforward semantic searches. “Find documents about climate change” doesn’t need relationship traversal.
• Your data lacks rich relational structure. A collection of independent blog posts or articles won’t benefit from a knowledge graph.
• Speed and simplicity are priorities. Vector RAG is faster to implement, easier to maintain, and scales more predictably with unstructured data.
• Your users ask questions that any single document can answer. If the answer lives in one chunk, vector similarity is all you need.

Vector RAG excels at what it was designed for: finding semantically similar content quickly and efficiently. For many production applications, it’s the right architecture.

The Hybrid Approach: Why Most Production Systems Use Both

In practice, the most capable RAG systems don’t choose one or the other—they combine both retrieval strategies. The pattern typically looks like this:

1. Vector search for initial retrieval. Use semantic similarity to find the right neighborhood of your knowledge base.
2. Graph traversal for context expansion. Follow relationships from retrieved entities to build complete, structured context.
3. Enriched context for generation. Feed the LLM both the semantically relevant content and the relationship structure.

For example, answering “Who should I contact about issues with the payment API?” might work like this: vector search identifies the “payment API” entity, graph traversal follows maintainedBy and reportsTo edges to find the right contact, and related edges surface common issues and relevant documentation.

The Enterprise Challenge: Security and Access at Scale

There’s a dimension to this problem that most GraphRAG discussions overlook: in enterprise environments, the knowledge you need often spans multiple systems, departments, and security boundaries.

A centralized knowledge graph solves the accuracy problem. But a decentralized knowledge graph—a network of independently managed graphs that connect at query time based on rights and permissions—solves both accuracy and the data access problem that keeps most enterprise AI projects from reaching production.

This is where organizations typically get stuck: the security overhead of centralizing sensitive data for RAG is too cumbersome and risky, so LLMs end up running on a fraction of the available knowledge. Decentralized approaches let queries span data sources dynamically, with governance enforced at the data layer rather than at the application layer.

The result, as our research showed, is the jump from 95% accuracy with centralized graphs to 90-99% with decentralized ones—not because the graph structure is better, but because the system can safely access more relevant context for any given query.

How to Decide: A Practical Framework

Ask these questions about your use case:

Do your queries require connecting information across multiple documents? → GraphRAG

Does your domain have hierarchies, org structures, or explicit relationships? → GraphRAG

Are hallucinated connections between entities a serious risk? → GraphRAG

Do you need auditability and data provenance in your retrieval? → GraphRAG

Are your queries primarily “find me something about X”? → Vector RAG

Is your data mostly independent, unstructured documents? → Vector RAG

If you answered yes to questions from both groups, you likely need a hybrid approach. Most enterprise applications do.

Getting Started

The investment in building a knowledge graph pays dividends as your questions become more sophisticated and your data more interconnected. If you’re evaluating GraphRAG for your organization, start with a single high-value use case where vector RAG is clearly falling short—compliance queries, customer 360 views, or incident investigation are common starting points.

The key architectural decisions aren’t just about graph vs. vector. They’re about how you handle security across data boundaries, how you maintain trust in your data provenance, and how you scale access without centralizing everything into a single point of failure.

GraphRAG isn’t just an incremental improvement over vector search. For the right problems, it’s the difference between an AI system that finds relevant text and one that actually understands how your information connects.

Want to see how knowledge graph-based RAG performs on your data? Explore Fluree’s GraphRAG capabilities or read the full research report on decentralized GraphRAG accuracy.