Posted by technical_writer_karen
Our engineering team generates tons of documentation - SOPs, equipment manuals, maintenance procedures, P&IDs explanations - and we're drowning in it. I keep hearing about agentic AI that can supposedly write and update technical docs automatically but I'm skeptical. We tried having an intern just paste stuff into ChatGPT and the output was generic garbage that missed all the critical safety information. Has anyone successfully automated any part of their industrial documentation workflow using AI agents? I'm talking actual production docs that passed regulatory review, not just draft content. We're ISO certified and everything needs to be traceable and accurate so I can't just throw AI at it without serious validation. Curious if this is actually ready for prime time or still just hype.
Reply by automation_docs_specialist_brian** | 6 days ago
Karen I totally get your skepticism because we had the same concerns. The key is you can't just use a generic LLM, you need to build a proper agentic system that can access your actual engineering data. We implemented something using LangChain that pulls from our PLCs, reads existing CAD drawings, accesses our equipment database, and then generates documentation based on real system configuration. It's not fully autonomous though, we use it to create first drafts that engineers review and approve. Here's a simplified version of how we set up the document generation agent:
The agent can reason about what information it needs and pull from multiple sources. Way better than just prompting ChatGPT.
Reply by quality_manager_lisa
Brian's approach sounds solid but I want to stress the validation piece because this is where most companies screw up. You absolutely cannot just publish AI-generated docs without proper review, especially for safety-critical stuff. We built a three-stage approval workflow: AI generates draft, subject matter expert reviews technical accuracy, quality team verifies it meets documentation standards and regulatory requirements. The AI saves us probably 60-70% of the initial writing time but that last 30% of human review is non-negotiable. Also make sure you're keeping track of which sections are AI-generated vs human-written for your audit trails. Our FDA auditors wanted to see that documentation and we had to scramble to add that metadata after the fact.
Reply by technical_writer_karen
Brian that's really interesting, so the agent is basically doing research by pulling from internal systems before writing? That makes way more sense than generic prompting. What LLM are you using and how are you handling domain-specific terminology? We have tons of proprietary equipment names and process-specific jargon that I imagine would confuse a general model. Lisa the validation workflow you described is basically what I was thinking, glad to hear that's the right approach. Are you tracking time savings? I need to build a business case for this and "it saves time" isn't specific enough for our CFO.
Reply by automation_docs_specialist_brian
We're using GPT-4 through Azure OpenAI because we needed the data residency guarantees. For domain terminology we did two things: created a comprehensive glossary that gets included in the system prompt, and fine-tuned a custom model on about 500 of our existing approved documents. The fine-tuning made a huge difference, the model learned our documentation style and specific terminology. Here's part of our system prompt structure:
system_prompt = f"""You are a technical documentation specialist for industrial automation systems.
TERMINOLOGY STANDARDS:
{load_company_glossary()}
DOCUMENTATION REQUIREMENTS:
- Use ISO 9001 compliant structure
- Include all required safety warnings per OSHA 1910.147
- Reference specific equipment by model number and serial number
- All measurements in metric units unless specified otherwise
CRITICAL SAFETY RULE:
If you are unsure about any safety-related information, mark it as [REQUIRES SME REVIEW] and do not make assumptions.
Current task: Generate maintenance procedure for {equipment_type}
Available data sources: Equipment DB, PLC configuration, existing procedures
We explicitly tell it to flag uncertainties rather than hallucinate, that's been crucial for safety docs.
Reply by control_systems_engineer_james
One thing I haven't seen mentioned is keeping documentation in sync with actual system changes. We have the opposite problem where our systems get modified but nobody updates the manuals and then six months later nobody knows why something was changed. Are you guys running these AI agents continuously to detect when documentation is out of date? Like if I reprogram a PLC could the agent detect that and flag the corresponding SOP needs updating? That would be way more valuable than just initial doc creation IMO. Right now we have a massive backlog of "as-built" documentation that doesn't match reality and it's a compliance nightmare.
Reply by technical_writer_karen
Carlos that's brilliant, the automatic change detection would solve so many headaches. We're constantly playing catchup because engineers make changes and forget to tell us. How are you handling diagram updates though? A lot of our documentation is P&IDs, wiring diagrams, network topology drawings etc. I assume the AI can't generate those automatically right? Or can it? Also what kind of infrastructure do you need to run this? We're a smaller operation, I can't justify a massive GPU cluster or anything.
Reply by ml_infrastructure_dan
For diagrams we're using a hybrid approach - the AI can't draw P&IDs from scratch but it can update existing ones if they're in a structured format. We converted our CAD drawings to a JSON representation and the agent can modify that, then we re-render to PDF. It's janky but works for simple changes like adding a valve or updating a tag number. For completely new diagrams you still need a human. Infrastructure-wise you don't need much, we're running everything on a single Azure VM with 8 cores and no GPU. The LLM API calls are cloud-based anyway so local compute is just for the orchestration logic. Our monthly Azure OpenAI bill is around $400 and we're processing about 200 documents per month, so pretty cost effective compared to hiring another tech writer.
Reply by quality_manager_lisa
Going back to Karen's original question about ROI, we tracked metrics for six months and found that AI-assisted documentation reduced initial draft time from an average of 4 hours to 1 hour per procedure. The review and approval time stayed about the same at 2 hours. So we went from 6 hours total to 3 hours total per document. With our volume of about 150 new/updated procedures per year that's roughly 450 hours saved or about $30k in labor costs. The Azure costs were around $5k annually so definite positive ROI. The bigger benefit though was actually the consistency - all our docs now follow the same structure and style which makes audits way smoother. We passed our ISO recertification with zero documentation findings for the first time ever.
Reply by technical_writer_karen
This has all been incredibly helpful, thanks everyone. Sounds like the key points are: use agentic approach with access to real data not just prompting, maintain strict human review especially for safety content, track everything for compliance, and set up change detection to keep docs current. Going to pitch this to management with Lisa's ROI numbers as a starting point. One more question - did any of you face pushback from your technical teams about AI writing their documentation? I'm worried our engineers are going to think I'm trying to replace them or something.
