Language is a dynamic and evolving concept that shapes thought and perception. The increasing reliance on Natural Language Processing models necessitates careful consideration of their alignment with inclusive language practices. However, Large Language Models often perpetuate biases due to training on androcentric and stereotypical data, undermining fairness and inclusivity. To address this, we propose a novel Two-Pass Retrieval Augmented Generation RAG with Chain of Thought framework that first retrieves contextual, unbiased references from a created corpus of inclusive texts and then applies structured, step-by-step reasoning via CoT prompting to enhance inclusivity in LLM output. By systematically retrieving relevant, unbiased references and enforcing structured reasoning, the framework promotes the generation of more inclusive and less biased content. Both LLM and human based evaluation using structured prompts with metrics like gender assumption, gender neutrality and quality and relevance Score are utilized. The text completion and generation tasks demonstrate that the proposed framework reduces gender bias.

Abstract Against the backdrop of the growing adoption of Kanban in a variety of software development domains, this paper reports on how a Systems Thinking approach was used to implement Kanban within a business unit of a government organization. Based on an in‐depth case study in the development organization, we describe how Kanban practices such as visualization, manage flow, classes of service, and board design were implemented. Stages in the double diamond design thinking process of exploring an issue more widely (divergent thinking) and then taking focused action (convergent thinking) were incorporated to adapt Kanban practices such as board design. Positive experiences included improved visibility, improved collaboration, and improvements in lead time. Challenges included lack of team buy‐in, setting work‐in‐progress limits, and short‐term Kanban coach. We present the key lessons learned and offer practical recommendations on how teams can better produce knowledge work in today's fast‐paced dynamic business environment.