Artificial Intelligence (AI) and Large Language Models (LLMs) have attracted significant public attention since the release of chat Generative Pre-trained Transformer (GPT) 3.5 from OpenAI. LLMs, trained on extensive datasets, provide comprehensive text responses to user requests upon various tasks. In chemistry particularly, LLMs have shown promise in reaction and molecular structure prediction owing to their expansive knowledge bases and complex pattern recognition capabilities. Embedding a chemistry-specific knowledge base into LLMs can noticeably improve the general performance in chemistry-specific queries. For example, ChemCrow is a LLM chemistry agent used to predict organic molecule structures with desirable properties and assist with the planning and execution of their synthesis. While LLMs can assist with interpreting experimental data and providing theoretical insights, they are traditionally incapable of conducting physical laboratory tasks, such as mixing reagents or operating lab equipment. This limitation can be overcome with their ability to assist in this text-to-procedure transcription with comprehensive understanding. For example, a comparable accuracy to transformer models can be achieved using GPT-3.5 with few-shot learning, by including several ground-truth instances in the prompt. Yoshikawa et al. proposed CLAIRify, which generates χDL code from descriptive instructions using zero-shot GPT coupled with a 2 verifier that iteratively prompts error messages in the chat. This LLM-based planning tool was later implemented to Organa, an AI voice assistant system on an electrochemistryspecialized SDL configuration, allowing interaction with the SDL in natural language. Additionally, Coscientist, a multi-LLM intelligent agent (GPT-4), used function calling with an embedded function pool. The system selects the most appropriate function according to the prompt to generate scripts in Emerald Cloud Lab (ECL) Symbolic Lab Language (SLL) and Opentrons Python application programming interface (API). Nonetheless, the implementation of LLMs in SDLs remains inaccessible for most chemists due to the lack of available and readily usable prompt methodologies for experimental design, and/or the labor-intensive development involved in using opensource LLM models. Platforms like RXN and ChemIDE, while available for text-to-procedure translation, require specific SDL configurations to execute the generated code locally due to their hardware-agnostic nature. Moreover, some general procedures often lack exact mass or volume quantities, limiting the efficacy of current machine-readable transcription techniques or services. Indeed, this is also a laborious and timeconsuming practice for human researchers.