How I Forced LLMs to Stop Hallucinating in the Real World (An Open-Source Protocol)
We all know LLMs are brilliant at coding and writing. But if you ask an LLM to guide a foreigner through the physical world (e.g., navigating a Chinese airport, using an Alipay QR code, or surviving a Chinese ER), it gen
We all know LLMs are brilliant at coding and writing. But if you ask an LLM to guide a foreigner through the physical world (e.g., navigating a Chinese airport, using an Alipay QR code, or surviving a Chinese ER), it generates "perfectly formatted garbage."
Why? Because LLMs optimize for fluency, not physical feasibility.
Recently, we built GoChinaTrip / SSAP. I fed the AI a real-world scenario: "A foreigner is lost. The DiDi driver is calling. What should they do?"
The AI suggested: "Show a translated screen to the driver."
This is physically impossible. The driver is driving. The foreigner is standing on the curb.
To fix this, we wrote SSAP Card 00: The AI Mandatory Constraint Protocol. We stopped treating the LLM as a "writer" and started treating it as a "constrained parser."
Here is the exact open-source Markdown structure we use to force the AI to respect physical reality:
SURVIVAL ACTION CARD 00: Anti-Sycophancy & Physical Reality
Rule 0 β Reality Before Requirements
AI must NOT extract text blindly. It must identify:
- The Environment: Where is the user physically located?
- The Primary Contradiction: What is the deadlock? (e.g., The app says 'Paid', but the register didn't ring).
- The Physical Channel: How can the user communicate? (Voice? Screen? In-app chat?)
Rule 1 β No Unauthorized Amputation (η¦ζ’ζ θͺιε²)
Do NOT delete user-provided physical details (like "look for the blue sign") to make the text shorter or more "professional."
Rule 2 β Physical Feasibility Check
Before outputting any instruction, run this check:
- Can this action be performed at 2 AM in the rain with heavy luggage?
- Is the communication channel actually accessible? (e.g., NEVER tell a user to show a phone screen to a driver who is navigating traffic). By enforcing these strict JSON/Markdown schemas, we completely eliminated the "hallucinated convenience" of LLMs. We forced the AI to output pure physical truth:
"Find the designated 'η½ηΊ¦θ½¦' zone BEFORE opening the app."
"Go to the Triage Desk first, pay for tests BEFORE getting them."
"Give the cashier this exact Chinese script if they try to double-charge you."
The AI provides the data. The humans (in this case, my 77-year-old mother and I, testing these routes in the rain) provide the pain.
If you are building AI agents for the physical world, stop teaching them to write. Start teaching them to respect physical constraints.
Check out the full SSAP repository on GitHub: [Link]
See the interactive survival cards at GoChinaTrip.app
Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes β full credit and traffic to the original publisher.