AI-Powered Standard Operating Procedure Generation and Optimization Using Large Language Models and Chroma Databases in Chemistry

Creating clear and accurate standard operating procedures is essential for safety and consistency in chemistry laboratories and industrial settings. These documents guide users through equipment handling, experimental steps, and safety protocols, but are often time-consuming to produce and update. In this article, we describe a system that uses recent advances in artificial intelligence to assist in generating these procedures. The system retrieves relevant information from a curated database of chemical manuals, regulatory guidelines, and scientific texts, and uses it to generate customized documents based on user input. Users can select which sections to include, such as calibration methods or waste disposal steps, ensuring the output aligns with specific needs. Designed to minimize errors common in automated text generation, the system helps maintain accuracy and relevance. This approach simplifies documentation tasks in chemistry while supporting compliance and safety, offering a practical tool for researchers and professionals in laboratory.