A Framework for Evaluating AI-Integrated Research Software: Demonstration and Validation through CyberFaCES Smart Search

Large Language Models (LLMs) have shifted the focus of Research Software Engineering (RSE) from basic implementation to systematic selection of effective architectures within a vast landscape of stochastic solutions. This paper addresses the "paradox of choice" by introducing a broadly applicable evaluation and integration methodology. Our three-phase framework comprises "fail-fast" initial, "broad-spectrum" improved, and final optimal rounds, supported by a three-level case analysis and a four-step evaluation cycle. We validate this methodology by developing and integrating a Smart Search Engine into CyberFaCES online learning platform, providing a repeatable blueprint for RSEs to navigate complex AI configuration spaces. By sharing our research paths and publicly available implementation, we offer a structured strategy to move beyond ad-hoc experimentation toward reliable, AI-integrated research tools.