GraphRAG vs Conventional RAG: A Decision Matrix

Choose GraphRAG when target questions require relationships, entities, communities, or corpus-wide themes that conventional chunk retrieval repeatedly misses. Choose conventional RAG when questions can be answered from a few relevant passages and freshness, simplicity, or indexing cost matters more.

GraphRAG is not a universal upgrade to vector search. It adds an extraction and graph-construction pipeline whose own errors, cost, lifecycle, and evaluation must be justified by the query distribution.

What the two approaches retrieve

Conventional RAG typically chunks documents, embeds text, retrieves top-ranked chunks, optionally reranks them, and sends selected evidence to a model. It works well when lexical or semantic similarity can locate the passages needed for an answer.

Microsoft's GraphRAG reference implementation adds a structured indexing process:

1. split the corpus into text units;
2. extract entities, relationships, and claims with a model;
3. build a graph and detect hierarchical communities;
4. generate summaries or reports for those communities;
5. retain text and graph artifacts for several query modes.

Its local search combines entity-linked graph data with source text. Global search uses community reports in a map-reduce process for corpus-level questions. DRIFT search combines community context with more focused follow-up retrieval. The project also includes basic vector search for comparison.

Decision matrix

The words “usually” matter. A highly engineered multi-stage RAG pipeline can be expensive, and a constrained graph system can be small. Benchmark the actual implementation.

Queries that may justify GraphRAG

Create a labeled slice for questions such as:

• Which organizations connect these two events, and through which relationships?
• What themes recur across the entire collection, including minority themes?
• How did a policy, actor, or concept change across documents and time?
• Which clusters of entities share claims, risks, or dependencies?
• What indirect relationships explain an observed outcome?

These queries require evidence spread across passages whose wording may not resemble the question. Graph structure and community summaries can create retrieval routes that plain top-k similarity does not expose.

GraphRAG still has to cite original evidence. A plausible graph edge extracted by a model is not automatically an authoritative fact.

Queries that favor conventional RAG

Use the simpler pipeline for:

• exact policy, product, error, or procedure lookups;
• questions answered by one or two passages;
• rapidly changing documents with strict update objectives;
• small corpora where direct context or search is sufficient;
• workflows with limited indexing budget;
• cases where entity extraction quality is poor or the graph schema is unstable.

Microsoft's own GraphRAG documentation retains a basic vector-search mode and warns that indexing can consume substantial model resources. Its repository describes the implementation as a research project in maintenance mode rather than an officially supported product. Treat those facts as deployment inputs.

Compare local and global questions separately

Do not report one blended “GraphRAG accuracy” number. Split the benchmark:

A graph approach can win the global slice and lose exact lookups. Route queries only if the routing decision is itself testable and the fallback is clear.

Count the full indexing cost

Conventional RAG indexing commonly pays for extraction, chunking, embeddings, and storage. GraphRAG may additionally pay for model-based entity/relationship/claim extraction, clustering, community reports, graph storage, and reprocessing after updates.

Record:

source documents and tokens
model calls, input/output tokens, and retries
wall-clock indexing time
graph entities, edges, communities, and text units
storage by artifact type
incremental-update cost
query calls, tokens, latency, and cost

Normalize both per corpus version and per successful target query. A quality improvement that costs more may still be correct for high-value investigation, but the tradeoff must be visible.

Test graph-specific failure modes

Add cases for:

• two entities incorrectly merged or one entity split;
• an extracted relationship unsupported by its source;
• direction, date, or negation lost from an edge;
• a community summary omitting a minority fact;
• deleted source text remaining in a graph or summary;
• a new document changing community structure;
• permission boundaries crossed through shared entities;
• a query mode selecting broad summaries for an exact question.

Always retain provenance from graph artifacts to source text. Authorization must filter every artifact that can reach the model, not only the original chunks.

A practical benchmark plan

1. Freeze the corpus and questions

Start with a small representative corpus. Label at least four slices: passage fact, entity neighborhood, multi-hop relationship, and global theme. For each case, record expected source IDs and a reviewed answer or rubric.

2. Establish a strong conventional baseline

Use lexical or hybrid retrieval, sensible metadata filters, and reranking if candidate recall supports it. A weak vector-only baseline exaggerates the value of any alternative.

3. Build the graph pipeline

Inspect extraction samples before running the full corpus. Tune entity types and prompts to the domain. Version extraction prompts, models, clustering parameters, and community reports.

4. Evaluate every layer

Measure source recall, answer correctness, groundedness, citation correctness, refusal, latency, and cost. For the graph system, also audit entity resolution, relationship support, community coverage, and query-mode routing.

5. Run lifecycle tests

Add, update, and delete representative sources. Verify when both systems reflect the change and whether old facts remain retrievable. Test access changes with shared entity names across tenants.

6. Choose by slice

Adopt GraphRAG only for slices where it produces a material, repeatable improvement worth its operational cost. A hybrid architecture may route global investigative questions to GraphRAG and exact questions to conventional search.

Production checklist

• Target graph-shaped queries are defined before implementation.
• Conventional RAG baseline includes reasonable lexical/hybrid retrieval.
• Graph extractions link back to original source spans.
• Entity and relationship quality is sampled by domain experts.
• Access control applies to chunks, graph artifacts, and summaries.
• Update and deletion objectives include graph-derived artifacts.
• Query modes have separate quality, latency, and cost reports.
• Failure slices include unsupported edges and summary omissions.
• Rollback can restore the prior corpus/index version.
• The added pipeline has a named owner and operating budget.

Before building either system, use when not to use RAG to confirm that search, direct context, or tools are not a better fit. Then apply the production RAG architecture guide and RAG evaluation metrics to the shared retrieval and generation layers.

Sources and further reading

• Microsoft GraphRAG documentation: process and query modes
• Microsoft GraphRAG: indexing overview
• Microsoft GraphRAG: query engine overview
• Microsoft GraphRAG GitHub repository
• Microsoft Research: Project GraphRAG