Domain knowledge graphs as scaffolds for LLM reasoning

Retrieval-augmented generation is the default answer to “how do I make an LLM stop hallucinating.” Index your documents, retrieve the top-k chunks, stuff them into the context window, and let the model generate. It works surprisingly well on broad domains, customer support, legal search, internal wikis. It works much less well on narrow, technical, control-heavy domains. There is a structural reason for this, and it points to a different design.

Retrieval over a corpus assumes that the right answer is somewhere in the corpus, expressed in roughly the right words. In a narrow domain like greenhouse climate control, the right answer is almost never expressed in the corpus. The corpus has fragments. It has a paper on vapour-pressure deficit, a manual on a specific climate computer, a PhD thesis on tomato transpiration, an FAQ on dehumidification. The operator’s actual question, “why did the controller open the ventilation flap right now,” is a composition of those fragments, and the composition is the hard part.

Knowledge graphs are good at exactly that compositional layer. A graph is a set of entities and a set of typed relations between them. For a greenhouse, the entities are sensors, actuators, climate variables, plant physiology states, and constraints. The relations are things like “ventilation flap actuator influences humidity variable,” “humidity variable affects fungal-disease risk,” “fungal-disease risk is constrained below threshold X for cultivar Y.” That is a small graph, a few hundred nodes, a few thousand edges. You can build it by hand with a domain expert in two afternoons.

The interesting move is what you do with the graph at inference time.

Naive use is bad. If you simply dump the entire graph into the context window, you have just made the LLM read a long, structured document, and you are back to the corpus-retrieval problem with extra steps.

The right use is constrained. The graph becomes a vocabulary that the LLM is allowed to talk about. When the model generates an explanation, it is required to express the explanation in terms of graph nodes and edges. Anything that does not reduce to the graph is flagged as ungrounded. The graph is not extra context. It is a contract about what the model is allowed to claim.

This is the move our 2025 Smart Agricultural Technology paper makes. We pair a model predictive controller with an LLM, and the LLM is forced to stay inside the domain knowledge graph when it explains a control action. The controller decides what to do. The graph decides what the explanation is allowed to say. The LLM does the linguistic gluing.

Three things become easier once you do this.

Verification. You can check that every entity and every relation in the explanation actually exists in the graph. If the model invents a new variable, the verifier catches it. This eliminates a whole class of confident-sounding hallucinations.

Editing. When the domain expert disagrees with an explanation, they can change the graph. They cannot easily change a 70-billion-parameter language model. The graph gives the human a steering wheel that the model cannot ignore.

Cross-domain reuse. The LLM stays the same. The graph swaps. Move from greenhouse to building HVAC and you swap the entities and relations, you do not retrain anything.

The cost is real. Building the graph is the unglamorous part of the work. It needs domain interviews, careful ontology decisions, and upkeep as the underlying plant changes. It also caps the system’s expressivity at whatever the graph contains. If the graph does not have a node for “leaf wetness,” the system cannot explain in terms of leaf wetness, even if the underlying physics involves it. That is a feature, not a bug, in safety-critical contexts. The system fails visibly, in the graph, where a human can see it, rather than invisibly, inside a transformer, where they cannot.

The pattern generalises beyond control. Any domain where the corpus is sparse, the variables are well-defined, and the cost of hallucination is high, is a domain where knowledge graphs as scaffolds beat retrieval over text. Medicine, manufacturing, energy systems, all of them fit. The trick is having the patience to build the graph.