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The study evaluates the performance of pre-trained large language models (LLMs) in construction cost estimation, comparing zero-shot prompting with a modular chain-of-thought (CoT) framework. Results indicate that the CoT framework significantly improves accuracy and reliability in cost estimation tasks, providing a structured approach to enhance LLM interactions. This research contributes a scalable framework for integrating AI into construction workflows, addressing the need for automation and reducing ma

Author

Ruchika Chaudhari

Language

EN