LLMs + Manufacturing Knowledge Bases: Beyond the Chatbot

Every plant has a treasure trove of operating knowledge — SOPs, deviation logs, incident reports, maintenance manuals, P&IDs, MSDS sheets, audit observations. None of it is accessible when the operator needs it at 2 AM. LLMs change that, if deployed correctly.

The Reference Stack

• Document ingestion: SOPs, manuals, deviation logs, structured data exports.
• Chunking with section-aware splitters.
• Embeddings stored in a self-hosted vector database (pgvector or Qdrant).
• Retrieval + re-ranking before LLM call.
• LLM call to Claude / Gemini / GPT (depending on use-case) via private endpoint.
• Citation: every answer carries the source document and section.

Evaluation Discipline

Without evaluation, LLM knowledge bases drift. The discipline:

1. Build a 200-question evaluation set with subject-matter-expert-approved answers.
2. Re-run the eval before every change — chunking, embedding model, LLM.
3. Track answer accuracy, hallucination rate, citation accuracy and latency.
4. Block any change that regresses on any metric without explicit override.

Operating Patterns That Work

• Maintenance Q&A — "How do I replace the seal on Pump P-204?" returns the exact procedure with citations.
• Deviation triage — "What past deviations look like this one?" returns nearest neighbours with outcomes.
• Compliance Q&A — conversational interface to the Form-12 / 15 / 25 obligations.
• Onboarding — new engineers ramp 2 — 3x faster with a knowledge-base copilot.