Fuzzing is an automated testing technique that generates random input to identify software bugs and vulnerabilities by provoking unexpected behavior. Although effective, traditional fuzzing lacks input generation guidance, which often leads to inefficiency and wasted time, especially for complex programs, because many inputs are invalid and are rejected. Grammar-based fuzzers address this problem by generating inputs that match the syntactic structure of the program, although they require expert knowledge to define accurate grammars. Large Language Models (LLMs) show remarkable capabilities in Natural Language Processing (NLP), improving efficiency in various domains. These models can be used to generate input for fuzzers, as they can quickly learn or already have familiarity with the required input formats. This paper explores the integration of LLMs with fuzzing methods to streamline directed input generation and thereby increase fuzzing efficiency. We specifically adapt Llama2 for use with American Fuzzy Lop (AFL), focusing on Extensible Markup Language (XML) due to its commonality as a structured file format. Our approach demonstrates the potential of LLMs to enhance traditional fuzzing by providing targeted, intelligent input generation. Experimental results show that our approach can achieve up to six times more code coverage after 24 h compared to using AFL alone. Furthermore, in our tests, our method provides up to 50% more coverage than a grammar-based fuzzer.