This repository contains the code and dataset for the study "From Text to Concrete: Developing Sustainable Concretes with In-Context Learning". The project aims to improve the development of sustainable concrete formulations using in-context learning (ICL) and large language models (LLMs). By leveraging the potential of LLMs, this research aims to overcome the limitations of traditional methods and accelerate the discovery of novel, sustainable, and high-performance materials. Overview The primary goal of this study is to compare the prediction performance of compressive strength using ICL and the text-davinci-003 model against established methods such as Gaussian Process Regression (GPR) and Random Forest (RF). The dataset comprises 240 alternative and more sustainable concrete formulations based on fly ash and ground granulated slag binders, along with their respective compressive strengths. Key findings of this study include: ICL performs just like GPR and matches the performance of RF when supplied with small training data sets. Fine-tuning LLMs with general concrete design knowledge reduces prediction outliers and outperforms RF.