A Structured Retrieval Augmented Large Language Model Framework for Automated Engineering Technical Bid Evaluation

Abstract

In the construction sector, technical bid evaluation is crucial for maintaining equity, evaluating contractor capabilities, and allocating resources efficiently. However, traditional methods rely heavily on manual expert review, which can introduce subjective biases and efficiency bottlenecks. While the existing literature predominantly emphasizes multi-criteria decision-making (MCDM) techniques, automated content-level scoring mechanisms remain largely underexplored. This study aims to develop an evidence-grounded automated technical bid evaluation framework by integrating Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs), addressing the challenges of criterion–evidence alignment and traceable scoring in engineering document evaluation. We propose a criterion-guided framework that integrates a structured scoring criteria database, semantic retrieval, and LLM-based reasoning to identify relevant evidence and generate evidence-supported evaluation results. Empirical results on 37 real-world technical bids indicate that the proposed framework achieves better performance over baseline models in terms of absolute error (MAE), concordance (Lin’s CCC), and citation accuracy (CA), while maintaining comparable ranking consistency. The contributions of this study lie in adapting RAG and LLM techniques to the engineering technical bid evaluation scenario, establishing a criterion-guided and evidence-grounded evaluation mechanism to enhance scoring traceability, and providing empirical validation using real-world engineering bid documents. This study provides empirical evidence for automated bid scoring, offering a practical technical reference for the digital transformation of construction bid evaluation.