Software testing is critical for ensuring software reliability, with test case generation often being resource-intensive and time-consuming. This study leverages the Llama-2 large language model (LLM) to automate unit test generation for Java focal methods, demonstrating the potential of AI-driven approaches to optimize software testing workflows. Our work leverages focal methods to prioritize critical components of the code to produce more context-sensitive and scalable test cases. The dataset, comprising 25,000 curated records, underwent tokenization and QLoRA quantization to facilitate training. The model was fine-tuned, achieving a training loss of 0.046. These results show the promise of AI-driven test case generation and underscore the feasibility of using fine-tuned LLMs for test case generation, highlighting opportunities for improvement through larger datasets, advanced hyperparameter optimization, and enhanced computational resources. We conducted a human-in-the-loop validation on a subset of unit tests generated by our fined-tuned LLM. This confirms that these tests effectively leverage focal methods, demonstrating the model’s capability to generate more contextually accurate unit tests. The work suggests the need to develop novel validation objective metrics specifically tailored for the automation of test cases generated by utilizing large language models. This work establishes a foundation for scalable and efficient software testing solutions driven by artificial intelligence. The data and code are publicly available on GitHub.