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

• Author(s): Savi Khatri
• Paper ID: 1708883
• Page: 578-585
• Published Date: 31-01-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 7 January-2023

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

Citations

IRE Journals:
Savi Khatri "Designing AI-Native Products: Building for A Post-ChatGPT World" Iconic Research And Engineering Journals Volume 6 Issue 7 2023 Page 578-585

IEEE:
Savi Khatri "Designing AI-Native Products: Building for A Post-ChatGPT World" Iconic Research And Engineering Journals, vol. 6, no. 7, Jan. 2023

APA:
Savi Khatri (2023). Designing AI-Native Products: Building for A Post-ChatGPT World. Iconic Research And Engineering Journals, 6(7).

MLA:
Savi Khatri "Designing AI-Native Products: Building for A Post-ChatGPT World" Iconic Research And Engineering Journals, vol. 6, no. 7, Jan. 2023.

BibTeX

@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}
}