AI Training for Manufacturing

This module shows where large language models are reliable and where they aren't. It also gives a factory a reasoned way to use them on purpose rather than by accident. This module gives office and plant staff the same starting picture before anyone opens a live tool.

• What a large language model is, without the vendor pitch
• Spotting a hallucinated number in a technical document before it ships
• Where AI already runs in your stack: Word, Outlook, Excel, the browser
• Predictive maintenance and machine vision, explained just enough to place them out of scope
• A short list of what to hand off, and a shorter list of what an engineer keeps
• One experiment, week one.

Role, context, task, format, checks: that's the five-part shape behind every prompt in this module, practiced entirely on your own work instructions, emails, and reports instead of invented examples.

• The five-part prompt shape, applied to a work order from your own plant
• Keeping a drawing, a recipe, or a spec out of the prompt itself
• Getting a second and third pass out of the model before anyone signs off
• Operator-facing versus customer-facing, choosing the register on purpose
• A prompt library that grows every time someone finds a good one

Work instructions, SOPs, nonconformance and 8D reports, and audit prep take up more engineering time than almost anything else on a shop floor. AI drafts a usable first pass on all of them. A review-before-release step closes every one of these flows.

• A work instruction, drafted from an engineer's or an operator's rough notes
• Keeping SOP revisions consistent when the underlying process changes
• An 8D or nonconformance report that keeps its root-cause reasoning intact
• Turning existing records into an ISO 9001 audit summary
• A work instruction repurposed as operator training material
• Where a wrong value gets caught before it reaches the floor

RFQs, delivery chasing, claim responses, and export paperwork make up the correspondence that leaves the building, and AI drafts the first version of nearly all of it. Export teams working in a buyer's language get the most out of this module.

• RFQ and price correspondence
• A supplier follow-up or delivery reminder, plus claim letters
• Customer and supplier correspondence, drafted and checked across languages
• Export paperwork drafted and checked, from proforma invoices to certificates of origin
• A long email thread, summarized down to the decision and the open action
• A template for the correspondence your team writes every week

Trade secrets, personal data, and quality discipline become daily habits here: what belongs in an AI tool, what never will, and how a policy gets written that people on the floor will use.

• What counts as a trade secret in a prompt, from a drawing to a customer list
• KVKK and GDPR, translated into what an office or plant worker does day to day
• A consumer AI login versus an enterprise one, and why the difference matters
• ISO documentation discipline once AI is drafting, covering revisions, approvals, and traceability
• Writing an AI usage policy people will actually read
• Keeping a log of AI-assisted work for the next audit

Getting from a room of trained people to a plant that uses AI well day to day: the pilots, the champions, the measurement, and the habits that hold up once the trainers are gone.

• Office pilots first, shop floor pilots on a deliberate delay
• A champions model that covers both the office and the shop floor
• Training around shift patterns
• Whether people changed how they work, weeks after the session
• A 90-day plan that turns a pilot into standard practice