Retrieval-Augmented Generation for Commercial Proposal Management in the Electrical Sector: An Engineering Project Management Evaluation
Resumen
This study evaluates the deployment of a Retrieval-Augmented Generation (RAG) LLM assistant for information management in commercial proposals for electrical generation plant retrofit projects. Using an experimental mixed-methods design framed by data-driven, risk-based, and value-driven project management lenses, the performance of the AI assistant was benchmarked against manual execution by proposal engineers. The results revealed that the RAG assistant reduced information processing lead time from a manual range of 2–30 minutes down to a predictable 1–2 minutes (a cycle-time reduction of approximately 90%), while maintaining a zero hallucination rate and generating a projected operating-cost saving of nearly 90% per cycle. In conclusion, RAG architectures provide a reliable and efficient decision-support instrument to enhance information management in pre-project engineering workflows.
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Derechos de autor 2026 Nefi Alejandro Barron Herrera , Elsa Marias de LA Calleja Mora
Esta obra está bajo licencia internacional Creative Commons Reconocimiento 4.0.
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