Retrieval Augmented Generation

Wiring a language model to the alert pipeline: the work order it can draft, and the diagnosis it still cannot

Issue 11 read the AI-in-manufacturing pitch from the floor and left one bounded test open: put a small local model where the pitch insists it belongs, on the alert the stack already produces, and measure what it adds. This issue runs that test on paper. When a gated flag fires, a small model turns the raw alert and the machine's manual into a serviceable first-line work order, the document task the FactoryBench results say it can actually do. Ask the same model in the same loop to name the fault from the signal and it walks straight into the 17.7 percent task it cannot. The value is real and the boundary is exact, and the design that earns the model its place is the one that never lets it cross the line.

Issue 11 set the franchise beat, the intersection of AI and manufacturing read from the floor, and closed by naming the first test: take the alert the reference condition-monitoring stack already produces and wire a small local language model to it, then model what the model adds rather than what a vendor demo claims. This issue runs that test. The finding is that the loop contains two tasks the agentic pitch keeps fused and that the model handles them at opposite ends of its competence. The first task is document retrieval. When a gated anomaly flag fires, the model receives the alert, the affected asset identifier, and the relevant section of the maintenance manual, and it drafts a first-line work order, naming the asset, quoting the applicable procedure, listing the parts, the tools, and the lockout and tagout steps, and leaving the priority and the assignment to a human. Published work on retrieval-augmented generation over maintenance and equipment documentation reports that small models do this reliably, because the task is retrieving and reformatting text the manual already contains rather than reasoning about the machine. The second task is diagnosis, deciding from the vibration signature what is actually wrong, and that is the exact task FactoryBench measured the best current models at 17.7 percent on, because reading an industrial time-series signal and retrieving a protocol document are separate competencies these models conflate. The same small model in the same alert loop can draft the paperwork and cannot name the cause, and the danger is that a confidently phrased wrong cause is indistinguishable in tone from a right one, so a model allowed to guess the fault will file plausible fiction into the maintenance record. The issue models the economics from the same edge-inference benchmarks issue 11 cited, a one-to-three-billion-parameter model returning single-digit to low-double-digit tokens a second on an owned edge box, which is fast enough to draft a work order in the seconds after a flag and far too slow to reason in a real-time loop, at a hardware cost that amortizes to a few dollars a month and adds nothing to the per-query bill because there is no per-query bill. The design conclusion is a single rule: the model may quote the manual and may never infer the cause, and the pipeline enforces the rule structurally by giving the model the alert and the document and withholding the raw signal it would need to hallucinate a diagnosis from. The numbers in this issue are modeled from published third-party benchmarks and vendor specifications, cited inline.