In the rapidly advancing field of artificial intelligence, software development has emerged as a key area of innovation. Despite the plethora of general-purpose AI assistants available, their effectiveness diminishes in complex, domain-specific scenarios. Noting this limitation, both the academic community and industry players are relying on contextualized coding AI assistants. These assistants surpass general-purpose AI tools by integrating proprietary, domain-specific knowledge, offering precise and relevant solutions. Our study focuses on the initial experiences of 62 participants who used a contextualized coding AI assistant -- named StackSpot AI -- in a controlled setting. According to the participants, the assistants' use resulted in significant time savings, easier access to documentation, and the generation of accurate codes for internal APIs. However, challenges associated with the knowledge sources necessary to make the coding assistant access more contextual information as well as variable responses and limitations in handling complex codes were observed. The study's findings, detailing both the benefits and challenges of contextualized AI assistants, underscore their potential to revolutionize software development practices, while also highlighting areas for further refinement.

翻译：在人工智能快速发展的领域中，软件开发已成为创新的关键方向。尽管通用型AI助手种类繁多，但其在复杂、特定领域场景下的效能显著下降。针对这一局限，学术界与产业界正逐步转向情境化编码AI助手。这类助手通过整合专有领域知识，提供精准且相关的解决方案，其性能超越了通用型AI工具。本研究聚焦于62名参与者在受控环境中使用情境化编码AI助手——StackSpot AI的初始体验。据参与者反馈，该助手的应用显著节省了时间、简化了文档获取流程，并能为内部API生成准确代码。然而，研究发现与知识源相关的挑战依然存在——这些知识源需使编码助手获取更多情境信息，同时存在响应差异及处理复杂代码能力有限等问题。本研究的发现既阐述了情境化AI助手的优势与挑战，也凸显了其革新软件开发实践的潜力，并为后续优化指明了方向。