AI-Powered Standard Operating Procedure Generation and Optimization Using Large Language Models for Chemical Laboratory Applications

This paper presents an AI-powered framework that automates the generation of Standard Operating Procedures (SOPs) for chemistry laboratories. The proposed solution integrates Generative AI, large language models (LLMs), and a Retrieval-Augmented Generation (RAG) architecture enhanced with a Chroma database and prompt engineering techniques. Together, these components enable the creation of accurate, customizable, and context-aware SOPs tailored to specific laboratory equipment, materials, and processes. SOPs play a vital role in ensuring safety, consistency, and regulatory compliance in chemical operations. Our system allows users to select and customize sections, such as safety guidelines, calibration steps, or procedural details, while automatically retrieving verified information from trusted sources, including equipment manuals and scientific literature. This approach minimizes hallucinations and ensures factual precision, which is crucial in chemistry workflows. By combining LLMs with semantic retrieval, the framework significantly reduces the time and effort required for SOP preparation while maintaining high standards of accuracy and safety. The results highlight how GenAI can transform laboratory documentation, offering a scalable solution for research and industrial chemical applications.