The works leading up to this study by Kawchak, K. focus on Large Language Models (LLMs) or LMMs with hundreds of billions or over one trillion parameters that have shown to be (A) Capable of generating rationale answers to drug synthesis text prompts by three models: Nemotron-4-340B, GPT-4o, and Mixtral 8x22B . (B) Text prompts using document retrieval of synthetic journal and supplementary PDFs using ScholarGPT and ChatGPT-4o both achieved scores of 9.4 and 9.2 average scores respectively . (C) Images and document retrieval based on spectra, mass spectrometry data, and chemical reactions with the 3.5 Sonnet model received 10/10 scores for 4 out of five prompts, helping to bridge the gap for practical and effective conversational AI applications with a wider range of organic chemists .