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A vector-based RAG chat can answer “Which document mentions the launch date?” but struggle with “What are the main themes across these documents?” The difference is the work the question requires: finding a passage similar to the query versus connecting and summarizing evidence scattered across a collection. GraphRAG is designed for the second kind of question, but its richer index costs more to build and does not guarantee more accurate answers.
“Half” is a hook, not a measured failure rate: Microsoft’s published research does not establish that typical RAG systems miss half their answers. It describes a specific weakness in corpus-wide synthesis and evaluates GraphRAG on that class of task.
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Why semantic retrieval can miss answers that are in the corpus
In a common RAG setup, the system searches for passages semantically similar to the user’s question, then gives the best-matching passages to a language model. This works well when the question points toward a particular passage: for example, “What date does the project plan give for launch?”
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It is a less natural fit for a question such as “What are the top five themes in the data?” No single passage has to resemble that question. The answer depends on finding evidence across many documents, deciding which ideas recur or connect, and synthesizing them. A top-k retriever may return a few locally relevant chunks without capturing the collection-wide picture. Microsoft describes this as query-focused summarization rather than explicit retrieval, and also points to difficulty connecting disparate facts through shared attributes.
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This is a limitation of a retrieval pattern for certain question shapes—not evidence that all vector RAG systems fail, that the information is absent, or that GraphRAG removes hallucinations. Retrieval settings, corpus quality, and the model still matter.
What GraphRAG adds during indexing
GraphRAG builds a structured representation alongside the source text. In Microsoft’s approach, an LLM extracts entities—such as people, organizations, and places—and the relationships described between them. It groups related entities into communities and generates summaries, called community reports, for those groups. The standard indexing workflow can also include entity and relationship summaries and optional claim extraction.
At query time, the system can draw on those graph structures and summaries to assemble context. The original paper describes using community summaries to produce partial answers and then summarizing those into a final response. Raw source chunks remain relevant, particularly for questions focused on a specific entity.
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Choose the search mode that matches the question
GraphRAG provides different query modes for different jobs. Route by question shape rather than sending every query through the most expensive mode; the documentation does not specify a universal threshold for when to switch.
| Question or need | Mode | What it does |
|---|---|---|
| “What has this named person or organization done?” | Local search | Combines relevant graph-derived information with raw source text chunks for an entity-centered answer. |
| “What are the main themes across the dataset?” | Global search | Processes community reports in a map-reduce-style workflow to synthesize a corpus-wide answer. |
| A local question that would benefit from broader community context or follow-up exploration | DRIFT search | Adds community context to broaden local search. |
| A direct lookup where a relevant passage should answer the question | Basic/vector search | Uses ordinary vector RAG; GraphRAG includes a basic implementation that can serve as a comparison point. |
For example, “What is Novorossiya?” is entity-centered and fits local search. “What has Novorossiya done?” may require connecting related evidence, while “What are the main themes in the dataset?” calls for global synthesis. These examples illustrate the distinction; the right mode depends on the contents of your own corpus.
What Microsoft’s results do—and do not—show
Microsoft’s 2024 paper reports that GraphRAG produced more comprehensive and diverse answers than a conventional RAG baseline for a class of global sensemaking questions. The evaluated datasets were in the 1-million-token range. That figure describes the scale of those datasets, not a system limit or a recommended corpus size.
The result is scoped: it supports using GraphRAG for the kind of broad synthesis evaluated, not a claim that it improves correctness on every task, domain, or benchmark. The published materials discuss dimensions such as comprehensiveness, diversity of viewpoints, and support for answers with source context; they do not establish a universal accuracy advantage.
Account for indexing expense and query resources
GraphRAG’s structured index takes extra work to create. Microsoft warns that indexing can be expensive, and its methods documentation estimates graph extraction at roughly 75% of indexing cost. This is an implementation estimate in mutable documentation, not a dollar figure or a guaranteed ratio for every corpus or deployment.
Global search also has a resource tradeoff. More detailed, lower-level community reports can support more thorough answers, but may require more time and LLM resources. That makes global search a poor default for simple lookups that a cheaper retrieval path can handle.
FastGraphRAG trades detail for lower cost
Microsoft’s FastGraphRAG option uses NLP noun-phrase extraction and co-occurrence rather than much of the LLM-based reasoning in the standard approach. Microsoft describes it as cheaper but noisier. It may suit global summarization when high-fidelity graph exploration is not the priority; test whether the resulting graph is adequate for your task rather than assuming the lower-cost option is equivalent.
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Global search has an option to include general knowledge from outside the corpus. Microsoft’s documentation warns that enabling it may increase hallucinations. If answers must be grounded in your documents, keep that option disabled unless you have a specific reason to broaden the context.
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How to decide whether GraphRAG is worth piloting
Do not replace a working retrieval system just because it misses some questions. First identify whether the failures are direct lookups, entity-centered questions, or requests for cross-document synthesis. Then compare the approaches on questions your users actually ask.
- Assemble representative questions. Include straightforward passage lookups, questions about a specific entity, and corpus-wide prompts such as “What are the top five themes in the data?”
- Run the current baseline. Record which relevant source passages it retrieves and whether the answer covers the evidence needed for each question.
- Run GraphRAG with a fitting mode. Use local search for entity-centered questions, global search for collection-wide synthesis, and compare basic/vector search on direct lookups. Try DRIFT when broader context is useful for a local question.
- Compare answer quality and costs. Check whether answers cover relevant evidence and represent distinct themes or viewpoints, then record indexing effort and query-time resource use. Treat these as evaluation dimensions, not assumed GraphRAG wins.
- Choose a route per task. Keep the simpler path where it works; use GraphRAG where its cross-document structure provides a meaningful, validated benefit.
Start with a small corpus and inspect extracted entities, relationships, and reports. Microsoft recommends starting small because indexing can be costly. Validate answers against the underlying documents: the graph and its summaries are derived representations, not a substitute for source evidence.
Check the project’s support status before adopting it
As of the repository README accessed in October 2026, Microsoft describes GraphRAG as a research project largely in maintenance mode, says it will not accept new pull requests or implement new features, and characterizes the code as a demonstration rather than an officially supported Microsoft offering. This is a date-sensitive statement about the implementation and its support posture, not proof that the method is unusable or abandoned. Recheck the repository if support status is a deciding factor.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThe practical decision is therefore about fit: GraphRAG is a candidate when users need answers synthesized across scattered evidence, and its indexing and query costs are acceptable. For direct questions answered by a passage, ordinary retrieval remains a valid and simpler choice.
Quick Recap
Sources
- Microsoft Research: From Local to Global: A Graph RAG Approach to Query-Focused Summarization
- Microsoft Research paper: From Local to Global: A Graph RAG Approach to Query-Focused Summarization
- GraphRAG query modes documentation
- GraphRAG indexing documentation
- Microsoft GraphRAG repository and documentation
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