This thesis investigates the integration of Large Language Models (LLMs) into Requirements Engineering to enhance the overall management of software requirements. The research develops an extensive literature review that begins with the evolution of sequence modeling—from RNNs to the attention mechanism and encoder-decoder frameworks—and proceeds by examining a large number of the most recent approaches that apply Pre-Trained and Large Language Models to the key requirements engineering tasks—from elicitation and classification to ambiguity management, traceability and finally API recommendation. Along with this, a practical experiment on API tracing is conducted. The experiment employs a multi-step approach based on Goal-Oriented Requirements Engineering (GORE) to map low-level software goals—generated based on natural language project documentation—to specific API endpoints detailed in Swagger files.