Structured Text (ST), a core language in IEC 61131-3 for industrial automation, remains challenging to author manually due to its rigid syntax and domain-specific semantics. We present \textbf{STARK}-Structured Text Assistant for Reasoning and Knowledge, a knowledge augmented system for automatic ST code generation from natural language specifications. STARK integrates a Large Language Model(LLM) with a curated PLC code knowledge base and a dynamic few-shot prompting mechanism. Trained on a customized parallel dataset of Natural Language–ST Code pairs stratified by logical complexity, STARK identifies control intent, retrieves relevant domain knowledge, and synthesizes executable ST code. Evaluation using expert review and LLM-assisted analysis shows that STARK significantly outperforms baseline models in syntactic and semantic correctness for easy and medium complexity tasks. Error visualization reveals improved handling of negations, conditional structures, and temporal operators. Our results demonstrate the effectiveness of structured reasoning and knowledge integration in domain-specific code generation.