This chapter introduced ROSARROSAR, a custom GPT created by one of the authors to help researchers access continuous, unlimited feedbackFeedback on the drafts of their manuscripts. ROSAR, which stands for Review for Originality, Significance and Rigour, is trained using three key criteria outlined in the UK Research Excellence Framework, as well as similar standards from high-ranking journals across the world. Originality measures how much a manuscript contributes new insights or innovative methodologies to its field, while Significance assesses its potential to influence scholarly thought or policy. Rigour addresses the intellectual integrity and methodological robustness of the research. The chapter will explore how ROSAR applies these criteria in a structured, quantifiable way. Manuscripts are scored on a 300-point scale, with percentage averages determining star ratings—ranging from Four Stars (world-leading), Three Stars (Internationally Excellent), Two Stars (Internationally Recognised), and One Star (Nationally Recognised). ROSAR's review process is designed to triangulate information from external sources like journal rankings and metrics to ensure a rigorous evaluation. ROSAR’s capacitycapacity to provide tailored feedback is another key focus. Rather than offering general comments, it pinpoints specific sections of the manuscript that need improvement, fostering an interactive dialoguedialogue with researchers. By promoting this level of engagement, ROSAR not only assesses the quality of manuscripts but also supports authors in elevating their work from Two Stars or lower to potentially higher ratings. Using exemplar prompts, the chapter will discuss how this process cultivates continuous improvement, aligning scholarly outputs with the global best standards of originality, significance, and rigour.