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Import an MDG to create a Reference Model
Importing and exporting Reference Models
Editing RM Connector type properties
Stereotypes inheriting from other Element Types
Customizing Reference Model Properties
Customizing Reference Model Element Properties
aiExpert and Model Expert Reference Models
Using aiExpert feels very different from using a traditional Model Expert reference model. Both provide valuable insight into the quality of your modelling, but they do so in very different ways.
Deterministic validation with Model Expert
When you validate against a reference model in Model Expert, the results are precise and repeatable. The same inputs will always produce the same outputs. If your model violates a rule, it will always be flagged in the same way, every time. This makes Model Expert a reliable tool for enforcing strict modelling standards and compliance. See Using Reference Models.
With the addition of scripted rules, Model Expert can provide even more guidance to modelers, but there is a practical limit to how complex we can make a script. Too complex, and it will be hard to get right, and even harder to maintain.
Probabilistic feedback with aiExpert
By contrast, when you send the same data to an LLM through aiExpert, the feedback may vary. This is not an error â it is a reflection of how LLMs work. They generate answers based on probabilities across a vast knowledge base. One run might highlight naming conventions, another might suggest diagram clarity, even with the same inputs.
The question of hallucination
Another difference is the possibility of hallucination. This means the AI might produce feedback that sounds authoritative but is not actually grounded in the data or standards. While much less common with GPT-5, it can still occur. Thatâs why aiExpert always anchors its prompts in public standards, to reduce the risk â but it cannot eliminate it entirely.
How to use LLM feedback wisely
The key is to treat aiExpert feedback as guidance, not absolute truth. Always think critically before making changes to your model. If feedback seems unclear or irrelevant, check it against your own understanding of the standards, or run the query again. Often, the best results come from combining deterministic validation (Model Expert reference models) with the flexible insights of aiExpert.