  Large Language Models (LLMs) are the cornerstone in automating Requirements
Engineering (RE) tasks, underpinning recent advancements in the field. Their
pre-trained comprehension of natural language is pivotal for effectively
tailoring them to specific RE tasks. However, selecting an appropriate LLM from
a myriad of existing architectures and fine-tuning it to address the
intricacies of a given task poses a significant challenge for researchers and
practitioners in the RE domain. Utilizing LLMs effectively for NLP problems in
RE necessitates a dual understanding: firstly, of the inner workings of LLMs,
and secondly, of a systematic approach to selecting and adapting LLMs for
NLP4RE tasks. This chapter aims to furnish readers with essential knowledge
about LLMs in its initial segment. Subsequently, it provides a comprehensive
guideline tailored for students, researchers, and practitioners on harnessing
LLMs to address their specific objectives. By offering insights into the
workings of LLMs and furnishing a practical guide, this chapter contributes
towards improving future research and applications leveraging LLMs for solving
RE challenges.
