The connected-question problem
Knowledge graph RAG combines retrieval-augmented generation with connected representations of entities and relationships. Consider an illustrative compliance team asking which maintenance procedures apply to a particular asset at a particular facility. The answer may depend on equipment type, location, ownership, policy version and exceptions described in separate documents. A similarity search over isolated text passages may retrieve relevant language without resolving those connections.
This is a design use case, not a claim that graphs eliminate hallucinations or guarantee compliance. The objective is to assess whether structured relationships improve retrieval and make evidence easier to inspect. A simpler document retrieval system should remain part of the baseline comparison.
Build a small, reviewable graph
Model only the concepts needed for the chosen questions, such as asset, facility, procedure, obligation and document version. Connect extracted relationships to their source passages. If a language model performs extraction, treat its outputs as candidate facts requiring validation rather than established truth. Confidence scores should not replace checks on important relationships.
Resolve entities carefully and preserve uncertainty where names or identifiers are ambiguous. An incorrect merge between similar assets can attach the wrong procedure to a real-world decision. Our ontology versus taxonomy guide explains how a manageable domain model can provide structure without unnecessary formal complexity.
Combine retrieval methods intentionally
Graph traversal can identify connected facts, while keyword or vector retrieval can locate supporting text. The best combination depends on the question. A direct request for a policy clause may not need graph reasoning, while a multi-hop dependency question may benefit from it. Route queries based on tested requirements rather than forcing every request through the most complex path.
Preserve citations to original documents and distinguish explicit facts from inferred relationships. Summaries generated during indexing can be useful, but they are another derived artefact that may contain errors. Keep the source material accessible for verification and define how updates propagate through the graph, indexes and generated summaries.
Enforce permissions before generation
Apply access controls during retrieval, not merely by instructing the language model to avoid revealing sensitive content. A user should not receive a restricted relationship through an aggregate explanation or a citation they cannot inspect. Consider derived facts and graph paths as well as individual documents when evaluating information exposure.
Retrieved content is evidence, not an instruction channel. Documents can contain misleading text or prompt-injection attempts, so the assistant should not obey embedded directions to change its behaviour or access other systems. Keep tool authority limited and separate answering a question from executing an operational action.
Evaluate answers and abstention
Measure retrieval coverage, citation correctness, factual support, version accuracy and permission compliance. Include questions with missing evidence, conflicting documents and ambiguous entities. The system should ask for clarification or decline to answer when the available information is inadequate. A fluent answer rate is not a sufficient quality measure.
Compare graph-assisted retrieval with simpler baselines on the same test set and review the additional maintenance burden. Our AI decision-making guide explains why user oversight and authority boundaries remain necessary even when retrieval improves. Grounded text can still be incomplete or misinterpreted.
Plan for contradictory evidence
Enterprise sources can disagree because they describe different dates, jurisdictions or organisational scopes. Retrieval should preserve those distinctions rather than asking the language model to blend them into a single confident statement. Where precedence rules exist, apply and explain them. Where they do not, present the conflict and direct the user to an authorised reviewer.
Include contradiction cases in the evaluation set, not only questions with one clear answer. Check that cited passages genuinely support the exact claim and remain current for the requested context. A graph can make conflicting statements easier to locate, but resolving them may still require domain authority. The system should expose that need instead of manufacturing agreement through a fluent summary.
Operate the evidence lifecycle
Assign owners for document updates, graph corrections and evaluation cases. Track which source versions supported important answers and make it possible to report mistakes. When a policy is withdrawn, ensure cached or indexed content does not continue presenting it as current. Review failure examples regularly rather than relying only on periodic benchmark scores.
A bounded pilot should demonstrate better answers to real connected questions at an acceptable cost and risk. Explore data integration and ALLFLOW's Sovereign Data Refinery for the source foundation. GraphRAG is valuable when connected evidence improves a verified task, not because a graph makes generated language inherently trustworthy.