Purpose The increasing incidence of nephrolithiasis underscores the need for effective, accessible tools to aid urologists in preventing recurrence. Despite dietary modification's crucial role in prevention, targeted dietary counseling using 24-hour urine collections is underutilized. This study evaluates ChatGPT-4, a multimodal large language model, in analyzing urine collection results and providing custom dietary advice, exploring the potential for artificial intelligence-assisted analysis and counseling. Materials and Methods Eleven unique prompts with synthesized 24-hour urine collection results were submitted to ChatGPT-4. The model was instructed to provide five dietary recommendations in response to the results. One prompt contained all "normal" values, with subsequent prompts introducing one abnormality each. Generated responses were assessed for accuracy, completeness, and appropriateness by two urologists, a nephrologist, and a clinical dietitian. Results ChatGPT-4 achieved average scores of 5.2/6 for accuracy, 2.4/3 for completeness, and 2.6/3 for appropriateness. It correctly identified all "normal" values but had difficulty consistently detecting abnormalities and formulating appropriate recommendations. The model performed particularly poorly in response to calcium and citrate abnormalities and failed to address 3/10 abnormalities entirely. Conclusions While ChatGPT-4 shows potential in aiding the dietary management of nephrolithiasis, its current limitations in identifying and addressing abnormal urine values suggests that it is not ready for clinical application. The model's ability to generate accurate and comprehensive advice under normal conditions contrasts sharply with its inadequacy in contexts of abnormalities like those seen among stone-formers. This juxtaposition highlights the need for significant improvement through further training and rigorous validation. If and when ChatGPT is used in clinical settings, it will require precise prompt design and meticulous monitoring by physicians. Despite the hurdles, the model's strengths indicate that, with advances, it could eventually make a valuable contribution to patient care and the efficiency of clinical practices.