Aim/Purpose: This article investigates the process of identifying and correcting hallucinations in ChatGPT-4's recall of student-written responses as well as its evaluation of these responses, and provision of feedback. Effective prompting is examined to enhance the pre-evaluation, evaluation, and post-evaluation stages. Background: Advanced Large Language Models (LLMs), such as ChatGPT-4, have gained significant traction in educational contexts. However, as of early 2025, systematic empirical studies on their application for evaluating students' essays and open-ended written exam responses remain limited. It is important to consider pre-evaluation, evaluation and post-evaluation stages when using LLMs. Methodology: In this study, ChatGPT-4 recalled 10 times 54 open-ended responses submitted by university students, making together almost 50,000 words, and assessing and offering feedback on each response. Contribution: The findings emphasize the critical importance of pre-evaluation, evaluation, and post-evaluation stages, and in particular prompting and recalling when utilizing LLMs for educational assessments. Findings: Using systematic prompting techniques, such as Chain of Thought (CoT), ChatGPT-4 can be effectively prepared to accurately recall, evaluate, and provide meaningful, individualized feedback on students' written responses, following specific instructional guidelines. Recommendations for Practitioners: Proper implementation of pre-evaluation, evaluation and post-evaluation stages and testing of recall accuracy are important when using ChatGPT-4 for evaluating students' open-ended responses and providing feedback. Recommendation for Researchers: Recall accuracy needs to be tested, and the prompting process carefully revealed when using and researching LLMs like ChatGPT-4 for educational evaluations. Impact on Society: As LLMs continue to evolve, they are expected to become valuable tools for assessing student essays and open-ended responses, offering potential time and resource savings for educators and educational institutions. Future Research: Future research should explore the use of various LLMs across different academic fields and topics to better understand their potential and limitations in educational evaluation.