RAG and Knowledge Graphs: When GraphRAG Helps

A RAG knowledge graph adds explicit relationships between things, such as products, suppliers and requirements, to the evidence an AI retrieves before answering. It can help when a question needs connections across several documents, but it adds extraction, maintenance and evaluation work that simple passage search may not need.

RAG means retrieval-augmented generation: finding outside information to support an AI response. A knowledge graph represents entities, the things you care about, and relationships between them. fenbs can keep the team’s source-backed architecture decisions and evaluation lessons; it does not build or query a knowledge graph.

How do graphs complement document retrieval?

Ordinary retrieval might find passages that resemble the question’s wording or meaning. A graph can expose an explicit route between related facts. Instead of hoping one passage mentions everything, the application can follow a supplier to its component, then the component to the products that use it.

The source passages still matter. A relationship labeled “supplies” needs evidence showing what is supplied, to whom, and under which conditions. Without that connection to the original text, a graph can turn an extraction mistake into something that looks like an established fact.

“RAG versus knowledge graph” is therefore often the wrong choice. RAG describes how an answer uses retrieved information; a graph is one way to organize information that retrieval can use. Our RAG explanation covers the basic retrieval-and-answer pattern.

What does GraphRAG add to the process?

GraphRAG can describe graph-assisted retrieval broadly, while Microsoft’s GraphRAG project↗ (opens in a new tab) is a specific implementation. Its documented process extracts entities and relationships from text, groups related entities into communities and prepares summaries that can support later questions.

Think of two possible routes through a document collection. One starts with a particular company or product and gathers nearby evidence. Another starts with a broad question about patterns across the collection and uses summaries to build a wider view.

Microsoft’s local search↗ (opens in a new tab) combines graph information with source text around relevant entities. Its global search↗ (opens in a new tab) uses community reports for questions about the collection as a whole. These modes support different questions; neither removes the need to inspect supporting evidence.

You do not need that entire architecture merely because your documents contain links. A small set of structured relationships may be enough for one task. First decide what question ordinary retrieval fails to answer and what relationship information would change the result.

Which questions justify the extra work?

For a hypothetical manufacturer, a graph could help connect a supplier notice to affected components and product manuals. But a question about one manual’s reset procedure may still be answered more directly by passage retrieval. Keep both question types in your evaluation so complexity has to earn its place.

A graph also cannot tell you that an architecture proposal was approved merely because many documents discuss it. Keep that human decision in fenbs Decisions and rules, with its reason and source links, so connected assistants can distinguish evidence from authority.

What can go wrong with extracted relationships?

Entity resolution means deciding whether two mentions refer to the same thing. A company’s abbreviation and full name may need to become one entity, while two products with similar names must remain separate. Mistakes here can create a convincing route through unrelated facts.

Relationship meaning also matters. “Discussed replacing,” “approved replacing” and “completed replacing” describe different states. Preserve dates, scope and qualifications where the question depends on them. A graph that drops those details may answer with a relationship that was only proposed or has already expired.

• Require source references for relationships that contribute to an answer.
• Distinguish extracted statements from reviewed facts and human approvals.
• Test aliases, similarly named entities, negation and time-dependent claims.
• Retain enough evidence to explain why two entities were connected.

Do not mistake a tidy diagram for a verified dataset. Review a sample of extracted relationships against source passages, including failures. Record recurring mistakes as lessons in fenbs so the next assistant knows which extraction assumptions need special attention.

How should you test a graph-assisted approach?

Build a baseline using ordinary retrieval and run the same questions against the graph-assisted version. Include direct lookups, questions requiring several relationships, broad summaries and questions with no support. Hold the source collection and user permissions constant.

Inspect whether the added relationship changes the answer for the right reason. Check each step against source evidence, look for missing exceptions and review whether a summary overstates how common a theme is. Measure the time needed to prepare updates as well as the time needed to answer.

Give access tests special attention. If restricted documents contribute to a relationship or summary, returning that derived information can reveal something the user cannot read directly. Design access boundaries for derived records, and test permissions before supplying them to a model.

Finally, edit and delete sample sources. Check what happens to relationships and summaries derived from them. If your team cannot reliably update or remove the derived information, restrict the initial use case until it can. A successful demonstration is not yet a maintained retrieval system.

When is simpler retrieval the better choice?

Stay with a simpler approach when direct passages answer your real questions well, when relationship extraction is unreliable or when nobody can maintain the additional records. Improve source quality and retrieval before assuming a graph is the missing ingredient.

Separate current work state from reference knowledge, too. Agent memory versus RAG explains that boundary. A graph of documents does not automatically provide an assistant with the current task, an approved decision or a clear next step.

For choosing personal tools, see personal knowledge graph software. That guide addresses a different choice from adding relationships to a retrieval application.

The guides to a Claude Code knowledge graph and a second-brain knowledge graph cover their respective workflows.

How to do this with fenbs?

1. Create a free account, then add a project for the retrieval experiment and tasks for the baseline and graph-assisted versions.
2. Record the person’s architecture choice in Decisions and rules, including the question it should improve and links to the supporting evaluation.
3. Save Lessons learned about missed relationships, ambiguous entities and update failures. Keep sensitive source material in its approved storage.
4. Ask your connected assistant to read project context before continuing, then save Where we left off with the next comparison and any choice still waiting on a person.

fenbs is not the right fit for storing graph edges or running graph queries. Use it for the reviewed work around that system: why you chose it, what remains uncertain and which fixes the team should make next.

Find the surrounding retrieval and input-design topics in the context engineering hub.