From Chain-of-Note to Explainable Cognitive Prompting: Reproducible Multi-Layer Validation for Auditable LLM-Assisted PLC Programming : Structured Explainability and Deterministic Reasoning for Industrial PLC Applications

Abstract

Explainability in Large Language Model (LLM)–assisted PLC programming is essential for industrial adoption, where engineers must understand, validate, and maintain generated control logic under strict safety and standardization constraints. Existing explainability-oriented prompting approaches such as Chain of Note (CoN) encourage intermediate reasoning disclosures, but they remain loosely structured, difficult to audit, and weakly aligned with the formal semantics of IEC 61131-3 and industrial engineering practice. As a result, CoN-style explanations may improve transparency without guaranteeing semantic rigor, traceability, or deployment readiness. We introduce Explainable Cognitive Prompting (ECP), a prompting architecture that replaces narrative rationales with an engineering-aligned scaffold that separates requirement interpretation, control strategy, safety argumentation, and IEC 61131-3 code evidence, producing reviewable and traceable explanations during synthesis. We evaluate ECP against CoN using a three-layer protocol: LLM-in-the-loop (LITL) scoring by five independent LLM validators, blinded Human-in-the-loop (HITL) assessment by two certified PLC engineers under the same rubric, and Bilingual Evaluation Understudy (BLEU) as a supplementary lexical reproducibility signal. ECP achieves higher correctness and safety-related scores and increases inter-rater agreement (Cohen-style statistics), indicating more consistent interpretation by independent reviewers. Overall, ECP reframes explainability as an audit-oriented contract that improves verifiable Programmable Logic Controller (PLC) code generation and supports repeatable evaluation.