Manufacturing AI training
LLMs in manufacturing
A practical course on applying large language models such as GPT and Claude to unstructured plant data for knowledge search, fault diagnosis support, and automated reporting.
What does an LLM do in manufacturing?
Applying LLMs in manufacturing means putting GPT-class large language models to work on the unstructured data a plant already holds, such as equipment manuals, work orders, defect reports, and design documents, to deliver knowledge search, fault diagnosis support, design generation, and automated reporting.
RAG-based knowledge search
Manuals, SOPs, and repair history are embedded in a vector database so the LLM can find and summarize the relevant passage on the spot.
Unstructured data, handled automatically
The LLM analyzes and summarizes MES data and defect reports, taking the repetitive reporting work off people's desks.
Faster design exploration
Given a set of requirements, the LLM generates several design alternatives and compares their performance immediately, speeding up early development.
What you'll learn
The core material this course covers.
- Types of unstructured manufacturing data and where LLMs fit
- Generating design alternatives with generative AI
- RAG-based search across equipment manuals and SOPs
- LLM-generated defect reports and quality history summaries
- Fine-tuning and prompt engineering for manufacturing
- Automating process data analysis with AI agents
Curriculum
What we cover
- 1
The problems LLMs actually solve on the plant floor
Size up the volume and types of unstructured data you hold, then sort LLM use cases into knowledge search, diagnosis, design, and automation.
- 2
Design automation with generative AI
Learn the architecture that turns requirements into design alternatives, and practice how to validate what it generates.
- 3
Hands-on RAG system build
Embed manuals and work orders, store them in ChromaDB or Pinecone, and wire up a retrieval-and-generation pipeline with LangChain.
- 4
Automating plant data analysis
Use an LLM with tool use to automate SQL and sensor queries, and generate reports directly from MES data.
- 5
Adoption strategy and security
Compare on-premises and cloud deployment options, and cover how to protect confidential data and calculate ROI.
How teams put this to use
Design alternatives generated and validated on the spot
Given design requirements, the LLM produces several alternatives and compares their performance immediately, shortening the early exploration cycle.
An LLM chatbot for equipment fault diagnosis
Ask a question in plain language and the bot retrieves manuals and repair history through RAG, then walks the technician through the fix.
Automatic production and defect reporting
The LLM summarizes and analyzes MES data to generate daily and weekly reports and deliver them to the right people.
Expected outcomes
What changes once your team puts this to work on site.
- Less time searching manuals and SOPs means faster decisions on the floor.
- Automated routine reporting frees engineers for the work that matters.
- Generative AI shortens the cycle of exploring and validating new designs.
- Organizational know-how and tacit knowledge are preserved in an LLM knowledge system.
LLMs in manufacturing — FAQ
With a cloud API, your data leaves your network. For highly confidential material, we recommend installing an on-premises open-source model such as Ollama on your own server so nothing is sent outside. The course covers on-premises deployment options in detail.
For most manufacturers, RAG comes first. Fine-tuning needs a large volume of labeled data and GPU resources, while RAG works with the documents you already have and makes additions and edits easy, so the cost of getting started is far lower.
Yes. Sessions 1 and 2 cover LLM concepts and use cases in terms that non-technical staff can follow, and the hands-on work in sessions 3 and 4 comes with code templates, so basic Python is enough to keep up.
We'll design the training around your operation
Schedule, group size, and curriculum detail all get set with you.