IPC International Community magazine an association member publication
Issue link: https://iconnect007.uberflip.com/i/1545855
JULY 2026 I I-CONNECT007 MAGAZINE 55 The Recognition Moment You have probably seen this before. You ask AI to draft a customer response and it sounds polished, but overpromises. You ask for a material shortage note, and it misses the one fact the floor needs. You ask for an 8D, and it hands back a tidy report that sounds finished before the evidence is in. You gave it a task but never taught it your process. In a PCB shop, that matters more than people think. Our work is context-heavy, docu- ment-heavy, and exception-heavy. A generic answer can sound good while being wrong for your plant. What Context Actually Means When people hear "add more context," they assume it means making the prompt longer. That is not the point. Context is the information that teaches AI how to work inside your world, and it helps to treat it as a small library you reuse rather than a one-time paragraph. Here are the building blocks: 1. Plant profile: What kind of PCB shop are you: high-mix, quick-turn, aerospace-heavy, prototype-focused? What does the work generally look like? 2. Workflow summary: What process are we talking about? Who owns it? Where are the handoffs? What happens when things go wrong? 3. Source hierarchy: Which documents win if there is a conflict? Customer requirement, internal traveler, ERP record, or work in- struction? Tell the AI what to trust first. 4. Output format: Do you want an email, a risk summary, a checklist, an escalation note, or a draft 8D section? 5. Ask-before-answer instructions: Tell the AI to ask clarifying questions one at a time before drafting. This is where it becomes a thought partner, not a search engine. 6. Guardrails: Tell it what it is not allowed to do: invent capability data, make disposition decisions, claim evidence that is not in the record, or treat standards from memory as final authority. Why One Giant Chat Starts to Get Sloppy After a few good interactions, most people hit a snag: one long chat collects baggage. Your new request rides on top of old instructions, yester- day's problem, and a different tone, and the work drifts as stale history comes along. A long thread is like a conference table buried in notes from every meeting this week. Some still matter, some are outdated, and some belong to a different problem entirely. Stay in one chat while you are working on the same problem, and the history helps. Start a clean one when the task changes, the department changes, or the AI keeps dragging in irrelevant assumptions. When in doubt, make a new chat window. This is why a separate chat for each recurring job beats one catch-all thread. Give each job its own clean window. The sales chat receives the customer request and response rules; the material-issue chat receives the shortage facts and escalation format; and the 8D chat receives the complaint facts and evidence list. Start all three from the same base context—plant profile, terminology, source hier- archy, tone, capability boundaries—then add only what that job needs. Use CRIT so the AI Learns Before It Writes I introduced the CRIT framework in Article 1, and this is where it starts to earn its keep. • C = Context: Teach the plant, workflow, constraints, and source rules • R = Role: Tell the AI what perspective to take • I = Interview: Make it ask questions before it answers • T = Task: Define the exact output you need The Interview step is the one most people skip, and it is what keeps AI from acting more confident than it should. If the tool does not know enough, make it ask.