This paper illustrates Retrieval-Augmented Generation (RAG) as a powerful tool for organizational research, particularly in efforts by scholars and practitioners to engage in strategic knowledge management through assessment of timely, reliable, and comprehensive data. By integrating large language models (LLMs) with external knowledge retrieval, RAG tools enable users to overcome the limitations of traditional LLMs, such as hallucination risks, accuracy gaps, and reliance on outdated or incomplete training data. By dynamically retrieving and continually incorporating relevant documents into the generation process, RAG enhances the accuracy, relevance, and utility of generated responses by allowing for a more careful curation of external data sources. We perform an illustrative analysis of executive expertise, comparing RAG to other text analysis methods. In addition to offering a primer RAG usage, our illustration shows that RAG consistently delivers analytical results that are more accurate and contextually aware. Despite these advantages, RAG introduces complexity. We highlight RAG's potential to transform text analysis in organizational research, offering improved and more insightful interpretations than traditional methods, and providing a framework for application to numerous research and industry contexts.
