A State-of-the-practice Release-readiness Checklist for Generative AI-based Software Products
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 4 of its 42 references that are in the map. First posted on arXiv 27 Mar 2024.
Builds on
4 of 42 references.
References · 4
- 2023GPT-4 Technical Reportcited by 18,396
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
- 2021Generated Knowledge Prompting for Commonsense Reasoningcited by 214
- 2023Generative AIcited by 120
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Citing papers per year: 2024: 3.
3 papers.