  The advent of artificial intelligence has contributed in a groundbreaking
transformation of the fashion industry, redefining creativity and innovation in
unprecedented ways. This work investigates methodologies for generating
tailored fashion descriptions using two distinct Large Language Models and a
Stable Diffusion model for fashion image creation. Emphasizing adaptability in
AI-driven fashion creativity, we depart from traditional approaches and focus
on prompting techniques, such as zero-shot and few-shot learning, as well as
Chain-of-Thought (CoT), which results in a variety of colors and textures,
enhancing the diversity of the outputs. Central to our methodology is
Retrieval-Augmented Generation (RAG), enriching models with insights from
fashion sources to ensure contemporary representations. Evaluation combines
quantitative metrics such as CLIPscore with qualitative human judgment,
highlighting strengths in creativity, coherence, and aesthetic appeal across
diverse styles. Among the participants, RAG and few-shot learning techniques
are preferred for their ability to produce more relevant and appealing fashion
descriptions. Our code is provided at https://github.com/georgiarg/AutoFashion.
