This paper investigates the complexities of integrating Large Language Models (LLMs) into software products, with a focus on the challenges encountered for determining their readiness for release. Our systematic review of grey literature identifies common challenges in deploying LLMs, ranging from pre-training and fine-tuning to user experience considerations. The study introduces a comprehensive checklist designed to guide practitioners in evaluating key release readiness aspects such as performance, monitoring, and deployment strategies, aiming to enhance the reliability and effectiveness of LLM-based applications in real-world settings.
@article{arxiv.2403.18958,
title = {A State-of-the-practice Release-readiness Checklist for Generative AI-based Software Products},
author = {Harsh Patel and Dominique Boucher and Emad Fallahzadeh and Ahmed E. Hassan and Bram Adams},
journal= {arXiv preprint arXiv:2403.18958},
year = {2024}
}