What Is GraphRAG: A Breakdown of Microsoft's Technology
GraphRAG is a technology from Microsoft Research that transforms how AI handles large document collections. The system no longer simply searches for text — it understands the relationships between facts.
In this article, we explain what GraphRAG is, how it works, how it differs from standard RAG, and how it impacts AI-powered search.
What Is GraphRAG
GraphRAG is an approach to Retrieval-Augmented Generation (RAG) that combines information retrieval, a knowledge graph, and an LLM, enabling AI to locate relevant text fragments and understand the connections between them. The technology was introduced by Microsoft researchers Jonathan Larson and Steven Truitt on February 13, 2024.
Rather than working solely with isolated document chunks, GraphRAG builds a knowledge graph. The system analyzes all documents using an LLM, identifies key entities within them, and maps the relationships between those entities.
The foundation of such a graph consists of two elements: nodes and edges. Nodes represent entities — people, companies, products, locations, organizations. Edges describe the relationships between them: "developed," "works at," "owns," "certified by," "founded."
Knowledge graph visualization in the GraphRAG Visualizer tool from GitHub
GraphRAG then groups related entities into communities. For each community, the language model automatically generates a concise summary covering the key topics and relationships within the group — these ready-made summaries allow the system to quickly answer specific queries and analyze large datasets at a high level.
When a user submits a query, GraphRAG moves through several stages. The Query Processor identifies which entities and relationships are referenced in the query; the Retriever searches for relevant information in both the vector store and the knowledge graph; the Organizer selects the most relevant data and filters out noise; the Generator produces the final response based on the gathered context.
How GraphRAG Differs from Standard RAG
Classic RAG operates on a straightforward principle. Documents are split into small chunks, each of which is converted into a vector representation and stored in a vector database. When a user asks a question, the system retrieves the most semantically similar chunks and passes them to an LLM to generate a response.
This approach works well when the answer is contained within one or two closely related chunks. However, the system only sees isolated fragments and has no understanding of how they relate to one another. As a result, standard RAG frequently fails on queries that require combining facts from multiple different documents.
GraphRAG builds a knowledge graph from entities and the relationships between them. This allows AI to trace connections between people, companies, products, or events and generate a more comprehensive, coherent response.
The effectiveness of this approach was confirmed through Microsoft's testing on large datasets — podcast transcripts totaling approximately 1 million tokens and a news archive of 1.7 million tokens. GraphRAG demonstrated 72–83% higher answer comprehensiveness and 62–82% greater diversity compared to standard vector RAG, while using up to 97% fewer tokens for top-level summaries — making it not only more accurate but also more efficient.
For website owners, this means AI can consolidate brand-related information from across multiple pieces of content, increasing the likelihood that their content will be surfaced in a response.
Local Search and Global Search
Depending on the query type, GraphRAG employs different search modes — Local Search and Global Search.
Local Search is used when the goal is to find information about a specific entity or event. In this case, the system locates the relevant node in the knowledge graph and explores the objects and relationships connected to it. This approach works well for queries such as "Who founded the company?" or "What products does this brand offer?"
Global Search, by contrast, analyzes the entire document corpus. Rather than retrieving individual facts, it leverages pre-built community summaries to answer questions such as "What are the main themes in this document set?" or "What key trends can be identified?" This is one of the defining differences between GraphRAG and classic RAG.
In late 2024, Microsoft also introduced DRIFT Search — a hybrid mode that combines Local Search and Global Search. It allows users to first get a broad overview of a topic and then automatically drill down into a detailed analysis of specific entities, helping improve answer accuracy and reduce computational overhead.
GraphRAG in AI Search
GraphRAG is also actively reshaping AI search and SEO. The technology directly determines which brands and sources AI systems cite in their responses, and addresses the shortcomings of traditional search.
That's why webmasters and SEO specialists should keep a close eye on GraphRAG's development. AI models orient themselves around entities and the relationships between them — which is essentially the same set of principles that underpins Entity SEO, a topic we covered in a previous article.
Traditional search constantly gets tripped up by naming variations. Without a knowledge graph, an AI sees mentions of the same company under different abbreviations or spellings as entirely separate entities. GraphRAG consolidates all these variations into a single node, allowing the model to understand that they all refer to the same brand.
At the same time, this solves the attribution problem. The system clearly identifies the original source of information — making it possible to determine whether an AI will link directly to a brand or simply use a fact without crediting its author.
The same logic applies to relationships. Rather than relying on words simply appearing near each other in text, the graph captures real context. As a result, the AI sees a map of facts instead of a collection of disconnected fragments — and references the right brands in its responses far more consistently.
What Webmasters Should Do About GraphRAG
The emergence of GraphRAG doesn't mean websites need to rebuild their SEO strategy from scratch. Most recommendations align with practices already used to improve how search engines understand a site.
A consistent brand name across all platforms helps AI systems consolidate all mentions into a single entity, rather than treating different spelling variations as separate objects.
Explicit connections between people, products, companies, and topics within the content itself also matter. The fewer vague formulations and the more specifics, the easier it is for the knowledge graph to establish relationships between entities.
Schema.org markup for types such as Organization, Person, Article, and sameAs directly communicates to search engines and AI models which entities are present on a page, how they relate to one another, and which brand owns the content.
Wikidata and internal linking with descriptive anchors cover two additional angles: a Wikidata entry feeds directly into external knowledge graphs, while internal links with specific anchors signal the relationship structure within the site itself.
The Main Drawback of GraphRAG
Despite its advantages, GraphRAG has one significant drawback — the high cost of building a knowledge graph. The most resource-intensive stage is extracting all entities and relationships from documents, since this is performed by a large language model.
According to Microsoft's estimates, approximately 75% of all GraphRAG indexing costs fall on the graph extraction stage — the creation of the knowledge graph itself. This makes building such a system more time- and resource-intensive than classic vector RAG.
To reduce these costs, in November 2024 Microsoft introduced LazyGraphRAG — a streamlined version of the technology that optimizes graph construction and significantly cuts time and resource requirements without a noticeable drop in answer quality. This made the technology more accessible to companies working with large document volumes that cannot afford full indexing.
Conclusion
GraphRAG doesn't replace standard RAG — it complements it in scenarios where a complete picture needs to be assembled from dozens of different documents. Building the graph requires more time and resources, but in return the AI delivers significantly deeper and more accurate responses. That's precisely why this technology is already determining which brands make it into AI-generated answers.
