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

Year : 2024 | Volume :11 | Issue : 01 | Page : 45-53
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 the Number of Tokens. Journal of Artificial Intelligence Research & Advances. 2024; 11(01):45-53.
How to cite this URL: Prashant D. Sawant. A Real-time Visualization Framework to Enhance Prompt Accuracy and Result Outcomes Based on the Number of Tokens. Journal of Artificial Intelligence Research & Advances. 2024; 11(01):45-53. Available from: https://journals.stmjournals.com/joaira/article=2024/view=140247

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