Although \textbf{Large Language Models (LLMs)} have established predominance in automated code generation, they are not devoid of shortcomings. The pertinent issues primarily relate to the absence of execution guarantees for generated code, a lack of explainability, and suboptimal support for essential but niche programming languages. State-of-the-art LLMs such as GPT-4 and LLaMa2 fail to produce valid programs for \textbf{Industrial Control Systems (ICS)} operated by \textbf{Programmable Logic Controllers (PLCs)}. We propose \textbf{LLM4PLC}, a user-guided iterative pipeline leveraging user feedback and external verification tools – including grammar checkers, compilers and SMV verifiers – to guide the LLM’s generation. We further enhance the generation potential of LLM by employing Prompt Engineering and model fine-tuning through the creation and usage of \textbf{LoRAs}. We validate this system using a \textbf{FischerTechnik Manufacturing TestBed (MFTB)}, illustrating how LLMs can evolve from generating structurally-flawed code to producing \textbf{verifiably correct programs} for industrial applications. We run a complete test suite on \textbf{GPT-3.5, GPT-4, Code Llama-7B, a fine-tuned Code Llama-7b model, Code Llama-34B, and a fine-tuned Code Llama-34B model}. The proposed pipeline improved the generation success rate from 47% to 72%, and the Survey-of-Experts code quality from 3.0/10 to 7.2/10.