Mahaswetha S.
Shanmuga Pradeepan R.
Veeralakshmi M.
S. Rathnamala
- Student Department of Artificial Intelligence and Data Science, Sethu Institute of Technology, Virudhunagar, Pulloor Tamil Nadu India
- Student Department of Artificial Intelligence and Data Science, Sethu Institute of Technology, Virudhunagar, Pulloor Tamil Nadu India
- Student Department of Artificial Intelligence and Data Science, Sethu Institute of Technology, Virudhunagar, Pulloor Tamil Nadu India
- Associate Professor Department of Artificial Intelligence and Data Science, Sethu Institute of Technology, Virudhunagar, Pulloor Tamil Nadu India
Abstract
TaskPro AI is a chatbot made using the GPT-4.0 API key to revolutionize textual content creation for small businesses. Harnessing the power of generative AI, TaskPro AI is a versatile tool for generating various writing works, including blog posts, product articles, social media captions, and more. The chatbot streamlines content creation by providing users with an intuitive interface, making it effortless to articulate their ideas, thanks to its advanced capabilities. By highlighting keywords and understanding the context, TaskPro AI ensures the production of high-quality and engaging content tailored to the specific needs of each business. TaskPro AI empowers small businesses to elevate their online presence, captivate their audience, and stimulate growth by seamlessly integrating AI innovation into crafting compelling written content.
Keywords: Chatbot, GPT-4.0 API, generative AI, small businesses, textual content creation
[This article belongs to Journal of Software Engineering Tools & Technology Trends(josettt)]
References
- Wang Y, Pan Y, Yan M, Su Z, Luan TH. A survey on ChatGPT: AI-generated contents, challenges, and solutions. IEEE Open Journal of the Computer Society. 2023 Aug 16; 4: 280–302.
- Ray PP. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems. 2023 Apr 14; 3: 121–154.
- Kumar H, Musabirov I, Shi J, Lauzon A, Choy KK, Gross O, Kulzhabayeva D, Williams JJ. Exploring the design of prompts for applying gpt-3 based chatbots: A mental wellbeing case study on mechanical turk. arXiv preprint arXiv:2209.11344. 2022 Sep 22.
- Spasić AJ, Janković DS. Using ChatGPT standard prompt engineering techniques in lesson preparation: role, instructions and seed-word prompts. In 2023 IEEE 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST). 2023 Jun 29; 47–50.
- Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. 2014 Sep 1.
- Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. Advances in neural information processing systems. 2017; 1–11.
- Radford A, Narasimhan K, Salimans T, Sutskever I. (2018). Improving language understanding by generative pre-training. [Online]. https://gwern.net/doc/www/s3-us-west-2.amazonaws.com/d73fdc5ffa8627bce44dcda2fc012da638ffb158.pdf
- Jovanovic M, Campbell M. Generative artificial intelligence: Trends and prospects. Computer. 2022 Oct 1; 55(10): 107–12.
- Abedi M, Alshybani I, Shahadat MR, Murillo M. Beyond traditional teaching: The potential of large language models and chatbots in graduate engineering education. arXiv preprint arXiv:2309.13059. 2023 Sep 9.
- Kleebayoon A, Wiwanitkit V. ChatGPT and large language model (LLM) chatbots: Correspondence. J Pediatr Urol. 2023 Oct 1; 19(5): 605–6.
Journal of Software Engineering Tools & Technology Trends
| Volume | 11 |
| Issue | 01 |
| Received | February 16, 2024 |
| Accepted | April 11, 2024 |
| Published | April 23, 2024 |