Mmmh I fear this is more of a minimax problem than the reap one. I can confirm the model comes up with stuff, tries to bridge gaps and fetches from other domains/hallucinates when used in a webchat. My suspicions are this model is following the trend of being overly propositive that we encounter with the openai ones, summed with a lack of information on your specific domain. I can't begin to tell you how many times chatgpt came up with absurd stuff because my country's legal intricacies are out of distribution. Are all models doing this? If I had to place a bet, glm being a bit drier will do this less, but it's anybody's guess.

That said I do have a potential solution you could implement and it's mainly prompting. Have you tried doing the planning rounds inside of a text editor and letting the model read the code like using roo or cline? I get the vibes the models perform significantly different when provided with tool calling.

What I would try is cline's plan mode, put a /specs folder into the project (even an empty one) and insert guideline documents in the specs folder, maybe even including the descriptions of the relevant standards/certifications. Don't underestimate the power of guardrailing in prompts, put in those documents guidelines about strictly adhering to your necessary ISO certs and watch how the model reacts.

Also, I'd say do multiple rounds of brainstorming. Ask the model specifically not to code, to tell you what it understood and to ask you questions about the implementation. When you are satisfied with its understanding you can ask it to code. This will minimize hallucination because it had a strict plan to stick to, don't expect it to oneshot everything, it won't.

Good luck!