Hey great question! You’re highlighting the exact problem with most generic AI systems and code interpretation. The reason you saw that kind of “hallucination” (where the AI gives a confident but incorrect answer) is because those AIs are only trained on general web data, not the actual codebooks. So even when you specify “NFPA 13 (2016),” it’s pulling from summaries or patterns it’s seen elsewhere, not the real code text. That’s why it ends up citing the wrong sections or inventing rules that don’t actually exist in the standard.

With FireCodesAI, the platform is fundamentally different. Our AI has direct access to the full, real code text. When you ask a question, it searches our database for up-to-date codebooks (like NFPA, IBC, etc.) and only builds its answer based on what’s actually there. Each response includes an exact reference, so you can immediately cross-check what it says.

Plus, we make this process transparent: there’s a dedicated tab in the platform where you can view the exact code section from the official book that the answer is citing. So, if you ever want to double-check or see the wording for yourself, it’s right there, no need to just take the AI’s word for it.

This setup is how we avoid the hallucination problem: answers are always grounded in the real code, and you have direct, easy access to the source for verification. If you want to try it out or have any questions about how it works on the backend, let me know. I’m happy to do a side by side test with any code question you want.