  Large Language Models (LLMs) are becoming key in automating and assisting
various software development tasks, including text-based tasks in requirements
engineering but also in coding. Typically, these models are used to automate
small portions of existing tasks, but we present a broader vision to span
multiple steps from requirements engineering to implementation using existing
libraries. This approach, which we call Semantic API Alignment (SEAL), aims to
bridge the gap between a user's high-level goals and the specific functions of
one or more APIs.
  In this position paper, we propose a system architecture where a set of
LLM-powered ``agents'' match such high-level objectives with appropriate API
calls. This system could facilitate automated programming by finding matching
links or, alternatively, explaining mismatches to guide manual intervention or
further development.
  As an initial pilot, our paper demonstrates this concept by applying LLMs to
Goal-Oriented Requirements Engineering (GORE), via sub-goal analysis, for
aligning with REST API specifications, specifically through a case study
involving a GitHub statistics API. We discuss the potential of our approach to
enhance complex tasks in software development and requirements engineering and
outline future directions for research.
