A Real-Time Visualization Framework to Enhance Prompt Accuracy and Result Outcomes Based on Number of Tokens

Year : 2024 | Volume :11 | Issue : 01 | Page : 44-52
By

Prashant D. Sawant

  1. Director Ai-D Consultancy Melbourne Australia

Abstract

In the rapidly evolving domain of artificial intelligence (AI), the efficacy of user- generated prompts has emerged as a critical factor influencing the quality of model- generated responses.
Current methodologies for prompt evaluation predominantly rely on post-hoc analysis, which often leads to iterative prompting and increased computational overhead. Furthermore, the challenge of “prompt hallucinations,” where AI models produce irrelevant or nonsensical responses, persists as a significant impediment to effective AI utilization.
The present article introduces a novel framework that leverages real-time analysis of prompts (number of tokens to start with) to provide users with immediate feedback on prompt quality. The proposed system employs a dynamic scoring mechanism that assesses prompts against a comprehensive corpus and a set of predefined quality criteria, outputting a relative strength percentage or a 1-10 scale rating which can be displayed by colors (Red to Amber to Green). By integrating this framework into the AI interface, users can iteratively refine their prompts before submission, thereby enhancing the interaction efficiency and reducing the likelihood of hallucinatory outputs. This approach represents a paradigm shift from reactive to proactive prompt optimization, paving the way for more seamless and effective human-AI collaboration. Also, it may revolutionize the way users engage with AI systems, fostering a more productive and harmonious human-AI synergy.

Keywords: Real-time prompt analysis, Prompt optimization, AI interaction efficiency, Preventing prompt hallucination, Human-AI collaboration

[This article belongs to Journal of Artificial Intelligence Research & Advances(joaira)]

How to cite this article: Prashant D. Sawant. A Real-Time Visualization Framework to Enhance Prompt Accuracy and Result Outcomes Based on Number of Tokens. Journal of Artificial Intelligence Research & Advances. 2024; 11(01):44-52.
How to cite this URL: Prashant D. Sawant. A Real-Time Visualization Framework to Enhance Prompt Accuracy and Result Outcomes Based on Number of Tokens. Journal of Artificial Intelligence Research & Advances. 2024; 11(01):44-52. Available from: https://journals.stmjournals.com/joaira/article=2024/view=140247





References

  1. Amatriain, Prompt Design and Engineering: Introduction and Advanced Methods. arXiv:2401.14423 (2024).
  2. Huang, W. J. Yu, W. T. Ma, W. H. Zhong, Z. G. Feng, H. T. Wang, Q. G. Chen, W. Peng, X. C. Feng, B. Qin, and T. Liu, A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. arXiv:2311.05232 (2023).
  3. Sun, An RL Perspective on RLHF, Prompting, and Beyond. arXiv:2310.06147 (2023).
  4. Mosbach, Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation. arXiv:2305.16938 (2023).
  5. Dhuliawala, M. Komeili, J. Xu, R. Raileanu, X. Li, A. Celikyilmaz, and J. Weston, Chain-of-Verification Reduces Hallucination in Large Language Models. arXiv:2309.11495 (2023).
  6. Besta, N. Blach, A. Kubicek, R. Gerstenberger, M. Podstawski, L. Gianinazzi, J. Gajda, T. Lehmann, H. Niewiadomski, P. Nyczyk, and T. Hoefler. Graph of Thoughts: Solving Elaborate Problems with Large Language Models. arXiv:2308.09687 (2023).
  7. What are AI Hallucinations? IBM, https://www.ibm.com/topics/ai-hallucinations [Access on 14 March 2024].
  8. Salvagno, F. S. Taccone and A. G. Gerli, Artificial intelligence hallucinations. Crit. Care 27, 180 (2023).
  9. V. Bentley and C. Naughtin, Both Humans and AI Hallucinate – But Not in the Same Way. (2023). https://www.csiro.au/en/news/All/Articles/2023/June/humans-and-ai- hallucinate.
  10. Maleki, B. Padmanabhan and K. Dutta, AI Hallucinations: A Misnomer Worth Clarifying. arXiv:2401.06796 (2024).
  11. Bruno, P. L. Mazzeo, A. Chetouani, M. Tliba, and M. A. Kerkouri, Insights into Classifying and Mitigating LLMs’ Hallucinations. arXiv:2311.08117 (2023).
  12. Siriwardhana, R. Weerasekera, E. Wen, T. Kaluarachchi, R. Rana, and S. Nanayakkara, Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering. Trans. Associat. for Computational Linguistics 2023; 1,1 1 – 17.
  13. F. Gao, Y. Xiong, X. Y. Gao, K. X. Jia, J. L. Pan, Y. X. Bi, Y. Dai, J. W Sun, Q. Y. Guo, M. Wang, and H. Wang, Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv:2312.10997.
  14. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, T. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv:2005.11401.
  15. Prompt Engineering for Generative https://developers.google.com/machine- learning/resources/prompt-eng.
  16. Skulmowski and K. M. Xu, Understanding Cognitive Load in Digital and Online Learning: A New Perspective on Extraneous Cognitive Load. Educ. Psychol. Rev. 34, 171 – 196 (2022).
  17. Trotta, M. Ziosi and V. Lomonaco, The Future of Ethics in AI: Challenges and Opportunities. AI & Soc 38, 439 – 441 (2023).
  18. A. Shams, D. Zowghi and M. Bano, AI and the Quest for Diversity and Inclusion: A Systematic Literature Review. AI and Ethics (2023).
  19. Crowell, Why AI’s Diversity Crisis Matters, and How to Tackle It. Nature (2023).
  20. Howard and C. Isbell, Diversity in AI: The Invisible Men and Women. MIT Sloan Management Review (2020).
  21. Zowghi and F. da Rimini, Diversity and Inclusion in Artificial Intelligence. arXiv:2305.12728, (2023).
  22. S. Collins, How to Use Promptfoo for LLM Testing. The Deep Hub Feb, 2024, Medium; https://medium.com/thedeephub/how-to-use-promptfoo-for-llm-testing-13e96a9a9773.
  23. H. K. N. Leung and P. W. L. Wong, A study of User Acceptance Tests. Software QualityJ. 6, 137 – 149, (1997).
  24. V. Alto, Evaluating LLM-Powered Applications with Azure AI Studio. Medium https://medium.com/microsoftazure/evaluating-llm-powered-applications-with-azure- ai-studio-b3cec3eba322.
  25. B. Xia, Q. Lu, L. Zhu , S.U. Lee, Y. Liu, and Z. Xing, Towards a Responsible AI Metrics Catalogue: A Collection of Metrics for AI Accountability. Semantic Scholar, Corpus ID: 265352192, (2023).
  26. G. Berman, N. Goyal and M. Madaio, A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluations. arXiv:2401.17486 (2024).
  27. B. M. Xia, Q. H. Lu, L. M. Zhu, S. U. Lee, Y. Liu and Z. C. Xing, Towards a Responsible AI Metrics Catalogue: A Collection of Metrics for AI Accountability. arXiv:2311.13158, (2023).
  28. T. Shin, Y. Razeghi, R. L. Logan IV, E. Wallace and S. Singh, AutoPrompt: ElicitingKnowledge from Language Models with Automatically Generated Prompts. arXiv:2010.159801, (2020).

Regular Issue Subscription Review Article
Volume 11
Issue 01
Received March 26, 2024
Accepted March 27, 2024
Published April 5, 2024