GraphRAG Architecture and Evaluation

GraphRAG augments language-model answers with evidence retrieved through both text search and graph structure. It is most useful when relationships, multi-hop paths, or entity context matter more than semantic similarity alone.

You will be able to: Design a hybrid GraphRAG pipeline and test when graph traversal improves evidence coverage over text retrieval.

Not started3 min explanation

Visualize, practice, and deep-dive material are optional—use only what helps you learn.

Explanation

A focused 3-minute explanation using the topic's authored material.

Learning goals and prerequisites

After this lesson

Design chunking and query-transformation strategies
Build multi-stage hybrid and graph retrieval
Operate an authorized, evaluated, traceable RAG system

Helpful before starting

Embeddings and lexical retrieval
LLM context and tool interfaces
Basic graph and evaluation concepts

Start here

Production RAG & GraphRAG, in plain language

Chunking, query transformation, hybrid retrieval, reranking, entity resolution, and global graph search. Production retrieval requires document structure, multi-stage ranking, graph construction, evaluation, authorization, and operational lineage to work together.

For a small example, a question asks which supplier affects a delayed product. Vector search finds similar documents; graph traversal follows supplier-to-component relationships, then both evidence sets are cited. This is the mechanism to keep in view as the lesson becomes more technical. Before moving on, identify the input, transformation, output, and one observation that would falsify your conclusion.

Key points

Layout-aware parsing and metadata.
Recursive, parent-document, sentence-window, and proposition chunking.
Dense, sparse, hybrid, and reranked retrieval.

Use it well

When Production RAG & GraphRAG helps—and where it breaks

Policies and connected business entities answer multi-hop questions. Ingest with provenance, resolve entities, retrieve hybrid context, generate cited answers, and evaluate retrieval and faithfulness separately. A useful result still depends on checking the assumptions and evidence below rather than treating one successful output as proof.

Key points

Choosing chunk size by intuition alone. Better approach: Evaluate chunk strategies on realistic labeled questions.
Trusting LLM-extracted graph edges. Better approach: Validate types and retain evidence spans.
Using one top-K metric as system quality. Better approach: Measure recall, ranking, grounding, citations, latency, and slices.

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