Generative AI is transforming business applications by enabling natural language interfaces and intelligent automation. However, the underlying large language models (LLMs) are evolving rapidly and so prompting them consistently is a challenge. This leads to inconsistent and unpredictable application behavior, undermining the reliability that businesses require for mission-critical workflows. In this paper, we introduce the concept of prompt migration as a systematic approach to stabilizing GenAI applications amid changing LLMs. Using the Tursio enterprise search application as a case study, we analyze the impact of successive GPT model upgrades, detail our migration framework including prompt redesign and a migration testbed, and demonstrate how these techniques restore application consistency. Our results show that structured prompt migration can fully recover the application reliability that was lost due to model drift. We conclude with practical lessons learned, emphasizing the need for prompt lifecycle management and robust testing to ensure dependable GenAI-powered business applications.

As Artificial Intelligence (AI) continues to grow daily, more exciting (and somewhat controversial) technology emerges every other day. As we see the advancements in AI, we see more and more people becoming skeptical of it. This paper explores the complications and confusion around the ethics of generative AI art. We delve deep into the ethical side of AI, specifically generative art. We step back from the excitement and observe the impossible conundrums that this impressive technology produces. Covering environmental consequences, celebrity representation, intellectual property, deep fakes, and artist displacement. Our research found that generative AI art is responsible for increased carbon emissions, spreading misinformation, copyright infringement, unlawful depiction, and job displacement. In light of this, we propose multiple possible solutions for these problems. We address each situation's history, cause, and consequences and offer different viewpoints. At the root of it all, though, the central theme is that generative AI Art needs to be correctly legislated and regulated.

This study evaluates the implementation of a Generative AI-powered rule checking workflow within a graduate-level Building Information Modeling (BIM) course at a U.S. university. Over two semesters, 55 students participated in a classroom-based pilot exploring the use of GenAI for BIM compliance tasks, an area with limited prior research. The instructional design included lectures on prompt engineering and AI-driven rule checking, followed by an assignment where students used a large language model (LLM) to identify code violations in designs using Autodesk Revit. Surveys and interviews were conducted to assess student workload, learning effectiveness, and overall experience, using the NASA-TLX scale and regression analysis. Findings indicate students generally achieved learning objectives but faced challenges such as difficulties debugging AI-generated code and inconsistent tool performance, probably due to their limited prompt engineering experience. These issues increased cognitive and emotional strain, especially among students with minimal programming backgrounds. Despite these challenges, students expressed strong interest in future GenAI applications, particularly with clear instructional support.

OpenAI has hired four high-profile engineers away from rivals, including David Lau, former vice president of software engineering at Tesla, to join the company's scaling team, WIRED has learned. The news came via an internal Slack message sent by OpenAI cofounder Greg Brockman on Tuesday. Lau is joined by Uday Ruddarraju, the former head of infrastructure engineering at xAI and X, Mike Dalton, an infrastructure engineer from xAI, and Angela Fan, an AI researcher from Meta. Both Dalton and Ruddarraju also previously worked at Robinhood. At xAI, Ruddarraju worked on building Colossus, a massive supercomputer comprising more than 200,000 GPUs.

This week in tech news, Elon Musk and Donald Trump are back at it, warring over the passage of the president's sweeping tax bill and the Tesla CEO's threat to create a third political party. Whether the richest person in the world is successful in those efforts will largely depend on the recruitment of another star politician. In other news, we want to know if you use generative artificial intelligence to write your personal messages – in what circumstances, and how often? Email tech.editorial@theguardian.com to let us know. Elon Musk and Donald Trump have reignited their feud after the passage of the president's sweeping tax bill on 3 July.

In March, NewsGuard – a company that tracks misinformation – published a report claiming that generative Artificial Intelligence (AI) tools, such as ChatGPT, were amplifying Russian disinformation. NewsGuard tested leading chatbots using prompts based on stories from the Pravda network – a group of pro-Kremlin websites mimicking legitimate outlets, first identified by the French agency Viginum. The results were alarming: Chatbots "repeated false narratives laundered by the Pravda network 33 percent of the time", the report said. The Pravda network, which has a rather small audience, has long puzzled researchers. Some believe that its aim was performative – to signal Russia's influence to Western observers.

Microsoft and OpenAI are planning to announce Tuesday that they are helping to launch an AI training center for members of the second-largest teachers' union in the US, according to details about the initiative that appear to have been inadvertently published early on YouTube. The National Academy for AI Instruction will be based in New York City and aims to equip kindergarten up to 12th grade instructors in the American Federation of Teachers with "the tools and confidence to bring AI into the classroom in a way that supports learning and opportunity for all students," according to the description of a publicly accessible YouTube livestream scheduled for Tuesday morning. The YouTube page also lists Anthropic, which develops the Claude chatbot, as a collaborator on what's described as a 22.5 million initiative to bring free "AI training and curriculum" to teachers. The three AI companies and the union did not immediately respond to requests for comment about the information released on YouTube. On Monday, Microsoft and the union declined to share details ahead of an announcement planned for Tuesday morning in New York.

Republican strategist Matt Keelen and Democratic strategist Fred Hicks debate how passing the'big, beautiful bill' will impact the macroeconomy and the upcoming midterm election cycle. Should we be alarmed by the acceleration of "artificial intelligence" ("AI") and the "large language models" (LLMs) AI's developers employ? Thanks to AI I can provide a short explanation of the LLM term: "Imagine AI as a large umbrella, with generative AI being a smaller umbrella underneath. LLMs are like a specific type of tool within the generative AI umbrella, designed for working with text." The intricacies of AI and the tools it uses are the stuff of start-ups, engineers, computer scientists and the consumers feeding them data knowingly or unknowingly.

Writing longer prompts for an AI assistant to generate a short story increases psychological ownership, a user's feeling that the writing belongs to them. To encourage users to write longer prompts, we evaluated two interaction techniques that modify the prompt entry interface of chat-based generative AI assistants: pressing and holding the prompt submission button, and continuously moving a slider up and down when submitting a short prompt. A within-subjects experiment investigated the effects of such techniques on prompt length and psychological ownership, and results showed that these techniques increased prompt length and led to higher psychological ownership than baseline techniques. A second experiment further augmented these techniques by showing AI-generated suggestions for how the prompts could be expanded. This further increased prompt length, but did not lead to improvements in psychological ownership. Our results show that simple interface modifications like these can elicit more writing from users and improve psychological ownership.

Translating nuanced, textually-defined authorial writing styles into compelling visual representations presents a novel challenge in generative AI. This paper introduces a pipeline that leverages Author Writing Sheets (AWS) - structured summaries of an author's literary characteristics - as input to a Large Language Model (LLM, Claude 3.7 Sonnet). The LLM interprets the AWS to generate three distinct, descriptive text-to-image prompts, which are then rendered by a diffusion model (Stable Diffusion 3.5 Medium). We evaluated our approach using 49 author styles from Reddit data, with human evaluators assessing the stylistic match and visual distinctiveness of the generated images. Results indicate a good perceived alignment between the generated visuals and the textual authorial profiles (mean style match: $4.08/5$), with images rated as moderately distinctive. Qualitative analysis further highlighted the pipeline's ability to capture mood and atmosphere, while also identifying challenges in representing highly abstract narrative elements. This work contributes a novel end-to-end methodology for visual authorial style personalization and provides an initial empirical validation, opening avenues for applications in creative assistance and cross-modal understanding.