Two emerging approaches that could address these limitations are Retrieval-Augmented Generation (RAG) and the Multiprompt Instruction PRoposal Optimizer (MIPRO). RAG combines a retrieval system with a generative model, enabling LLMs to dynamically fetch or calculate relevant, up-to-date information from external databases or knowledge sources . In the context of chemistry, RAG could draw on calculations or curated databases to supply the LLM with accurate molecular data or specific molecular properties in real time . This external grounding could significantly reduce the likelihood of hallucinations by ensuring that the LLM has access to precise chemical data instead of relying solely on its potentially limited training set. RAG is potentially valuable for tasks like predicting properties using group contribution methods, where relationships between molecular structure and molecular properties are complex and require detailed, accurate data that an LLM may not robustly encode .