End-effectors are a set of subroutines ready to be invoked via HTTP requests. Some of these may involve information retrieval tasks (local or public database queries like the Materials Project ), while others could have tangible real-world impact, as we have shown in the demo (liquid handling robot, laser cutter, pump, gas valve, robotic arm), primarily the components in data collection. The automation of these devices is mostly handled by PyAutoGUI, a script that can simulate human mouse and keyboard actions. However, we expect this redundant step will eventually become obsolete, as most laboratory equipment should ideally provide a dedicated interface for AI access. Boiko et al. previously reported an exceptional work using ChatGPT4 to script Opentrons OT-2 liquid handling robot for organic chemicals synthesis. It is truly fascinating that the AI can independently design experiments, however, we noticed that providing too much freedom to LLMs could lead to some hidden troubles. Due to the intrinsic stochasticity of LLMs, the real-time generated OT-2 script may not perfectly align with the intended design. The greatest risk stems from our inability to discern whether the script deviates from the intended design without a meticulous line-by-line inspection. This is mostly because (i) LLMs are highly skilled at generating seemingly correct code, and (ii) many OT-2 codes are repetitive and somewhat abstract, as people couldn't freely rehearse the mixing process in their mind by looking at 'A1', 'A2'. Plus, the deviation from the intended design may not be flagged as an error since the syntax could still be correct, increasing the risk in long-term learning projects as tracing potential erroneous data points becomes a great challenge. As a mitigation strategy, we adopted a balanced approach: allow LLMs the freedom to devise the recipes (in CRESt, this is primarily driven by active learning), while having the ot2 scripts' framework fixed and preset by human experts.