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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteBetter retrieval is the right first fix when a RAG system does not surface the passage that answers the question. Better relationships, usually meaning a GraphRAG-style knowledge graph built from your own documents, earn their indexing cost when the answer depends on connecting entities across passages, following a chain of linked facts, or summarizing themes across a whole corpus. The official GraphRAG documentation keeps a plain vector mode alongside its graph modes, so the useful question is which path each type of question should take, not which architecture wins for everything.
Diagnose the failure before you change the architecture
The two fixes address different failures, so find out which one you have. Take a set of real user questions, each paired with the passage that answers it, and work through this sequence:
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- Check retrieval first. For each question, look at whether the answering passage appears in the top results sent to the model. If it does not, you have a retrieval problem. Chunking, embedding choice, query rewriting, metadata filters and reranking are the levers. They are cheaper to test than a graph index, so exhaust them on questions that fail for this reason.
- Check whether the answer needs several passages joined together. If the right passages are retrieved but the model still cannot answer, look at what the question requires. A question asking how one person, product or regulation relates to another entity across several documents is a relationship problem. A question that needs only one passage is not.
- Check whether the question is about the whole corpus. Questions such as “what themes recur across these reports” do not map to any single passage. This is the category GraphRAG’s global search is built for.
What GraphRAG adds to the pipeline
GraphRAG extends standard RAG with an indexing stage in which an LLM builds structure from your documents. Microsoft Research’s overview, published February 13, 2024, describes the core idea: the LLM creates a knowledge graph from a private dataset, and that graph helps prepare context for answers. The official documentation describes the indexing workflow in more detail.
Indexing workflow
- Documents are sliced into TextUnits, the chunks the system works with.
- An LLM extracts entities, relationships and claims from each TextUnit.
- The resulting graph is clustered hierarchically into communities.
- An LLM generates a community summary for each cluster. Global search reads these summaries.
The documentation recommends prompt tuning, so plan to adjust the extraction prompts for your domain rather than relying on defaults. Every LLM step in indexing carries a token cost, which is why indexing cost is the first thing to budget, as covered in the cost section below.
The four query modes
GraphRAG’s query engine is not one method. Each mode uses the index differently, and each suits a different kind of question.
| Query mode | What it draws on | Best suited to | Trade-off stated in the sources |
|---|---|---|---|
| Basic vector search | Vector retrieval over text passages, with no graph traversal | Direct questions answerable from one passage | No relationship reasoning; no separate cost figure given in the official documentation |
| Local search | Extracted graph information about an entity, combined with raw text chunks | Questions centered on a named entity and its nearby facts | Output quality depends on what extraction captured for that entity; no cost figure given in the official documentation |
| Global search | Community reports generated during indexing | Corpus-wide themes, patterns and summaries | Described as resource-intensive in the official documentation |
| DRIFT search | Community context within the query process | Questions that need community-level context alongside specific detail | Not characterized further, and no cost or quality figure, in the cited documentation |
Because basic vector search is one of GraphRAG’s own query options, you can compare it against the graph modes without standing up a second stack.
Decision guide by question type
- Single-passage factual lookup. Start with basic vector search or your current passage retrieval. A graph has little to add for this workload.
- Entity-centered questions. Evaluate local search against your vector baseline. Because it pairs graph information with raw text chunks, you can still check each answer against its source passages.
- Multi-hop questions across documents. Graph-informed or hybrid retrieval is the candidate to test. GraphRAG-Bench places multi-hop questions under complex reasoning, a category where results vary by system, so verify on your own questions rather than assuming a gain.
- Corpus-wide themes. Global search is the mode designed for this. Run it on a sample of the corpus before scaling to everything.
Hybrid and routed setups
Han et al., “RAG vs. GraphRAG: A Systematic Evaluation and Key Insights” (arXiv:2502.11371), report distinct strengths for each approach across question answering and query-based summarization, and they consider ways to combine them. One way to act on that is to classify each incoming question by workload, send factual lookups to passage retrieval, and route entity, multi-hop or corpus-level questions to graph-informed modes. A router adds a component to maintain and a new failure mode when it misroutes a question, so measure its accuracy on your own questions before trusting it.
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What the evidence does and does not establish
Microsoft’s 2024 evaluation
Microsoft’s February 2024 overview compared GraphRAG with baseline RAG using an LLM as grader and qualitative measures: comprehensiveness, source context and diversity. It reports improvements on those measures and similar faithfulness to baseline RAG. These are pairwise judgments made by a model on a set of questions. The article is an early evaluation by GraphRAG’s own developers, and it does not show that every graph system outperforms every vector system.
Independent systematic comparison
Han et al. (arXiv:2502.11371), with authors affiliated with Michigan State University, the University of Oregon and Meta, compare RAG and GraphRAG on question answering and query-based summarization. Their abstract reports distinct strengths across tasks rather than a single winner, which supports comparing the two per workload.
GraphRAG-Bench
GraphRAG-Bench, introduced June 6, 2025, organizes its tasks into fact retrieval, complex reasoning, contextual summarization and creative generation. It evaluates construction, retrieval and generation separately. Its project page notes that recent studies find GraphRAG can underperform vanilla RAG on many real-world tasks. The benchmark frames the central question as: “Is GraphRAG really effective, and in which scenarios do graph structures provide measurable benefits for RAG systems?”
Survey framing
The survey “Graph Retrieval-Augmented Generation: A Survey” (arXiv:2408.08921) divides the field into three stages: graph-based indexing, graph-guided retrieval and graph-enhanced generation. It explains why relational structure can matter, but it is background, not a production recommendation.
What the sources cannot tell you
None of these sources gives a dollar budget or latency target that transfers to another deployment. Treat any single figure quoted for GraphRAG as specific to its dataset, judge model and configuration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to test both approaches on your corpus
- Group your real questions by workload: passage lookup, entity-centered, multi-hop and corpus-wide. Include questions users actually ask, not only ones written for a demo.
- Build a baseline with your current passage retrieval. Record whether the answering passage appears in the retrieved set, separately from whether the final answer is correct and faithful to it. These are different failures and need different fixes.
- Index a small slice of the corpus with GraphRAG first, then run each query mode on the same questions with the same answer model.
- Score comprehensiveness, faithfulness, source traceability and cost for each mode, by workload. If you use an LLM grader, check its verdicts against human judgment on a sample.
- Only then choose a single mode, a hybrid or a router, and repeat the test after you change the corpus or prompts.
Costs, maintenance and project status
The Microsoft GraphRAG repository states: “GraphRAG indexing can be an expensive operation, please read all of the documentation to understand the process and costs involved, and start small.”
- Indexing spends LLM calls on extraction and summarization, so the bill is driven by how much text you index and how often you re-index.
- The official repository currently describes GraphRAG as largely in maintenance mode. It says the project will not accept new feature work and is not an officially supported Microsoft offering, and that its code is a demonstration. Bug fixes and dependency updates may continue.
Repository status can change, so check the GitHub page before committing to a build. If you adopt GraphRAG, plan to own support and upgrades yourself.
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