Generating documentation for industrial IoT systems requires parsing complex protocols like CAN, OPC UA, and MQTT. Unlike simple consumer JSON, industrial data is binary and cryptic. The real challenge is teaching AI to understand these structures while preventing dangerous hallucinations in technical specs.

Validation is the hardest part, not generation. A four-layer approach checks protocol definitions, physics-based logic, cross-references manufacturer datasheets, and requires human review for safety-critical components. This ensures accuracy before documentation reaches technicians who rely on it for troubleshooting.

The architecture involves parsing definitions, extracting context, and generating content for different audiences. Engineers need signal definitions while technicians require troubleshooting guides. The system must handle multi-protocol environments and regenerate documentation when systems change, treating docs like version-controlled code.

Key decisions remain open: on-premise versus cloud deployment, API-first versus UI-first development, and whether to open-source protocol parsers. Beta testing is underway to validate accuracy in automotive and manufacturing environments, with the goal of reducing manual documentation workload while maintaining safety standards.