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

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Open Access

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Year : April 5, 2024 at 3:54 pm | [if 1553 equals=””] Volume :11 [else] Volume :11[/if 1553] | [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] : 01 | Page : –

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    Prashant D. Sawant

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  1. Director, Ai-D Consultancy, Melbourne, Australia

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Abstract

nIn 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.

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

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Computer Technology & Applications(jocta)]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Computer Technology & Applications(jocta)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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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 jocta April 5, 2024; 11:-

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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 jocta April 5, 2024 {cited April 5, 2024};11:-. Available from: https://journals.stmjournals.com/jocta/article=April 5, 2024/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Journal of Computer Technology & Applications

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[if 344 not_equal=””]ISSN: 2229-6964[/if 344]

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Volume 11
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 01
Received March 26, 2024
Accepted March 27, 2024
Published April 5, 2024

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Views: 39

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