Designing AI-Native Products: Building for A Post-ChatGPT World

@article{1708883,
      author = {Savi Khatri},
      title = {Designing AI-Native Products: Building for A Post-ChatGPT World},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {7},
      pages = {578-585},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708883.pdf},
      abstract = {The emergence of large language models (LLMs) such as ChatGPT, Claude, and Gemini has tremendously altered the conception and delivery of digital products. This paper proposes a brief methodology for AI-native product design, wherein LLMs become the core logic of the system, interface design, and value creation. Going beyond feature augmentation, we lay bare a blueprint outlining AI-centric UX, architectural design, and product governance. Prompt engineering is considered, examining real integrations such as Notion AI or GitHub Copilot and discussing trade-offs at the system-level between latency, scale, and explainability. Deployment models and feedback loops will be illustrated by means of key SmartArt diagrams and Python-generated figures. The paper further considers the ethical angle: bias reduction, transparency, and user trust- all of which stand as pillars for sustainable AI adoption in post-ChatGPT ecosystems.},
      keywords = {AI-native design, generative AI, LLM-first architecture, GPT-powered apps, prompt engineering, retrieval augmented generation, explainable AI, human-AI interaction, scalable UX, product governance, neural UX, trust calibration, post-ChatGPT design},
      month = {January},
  }