Hamza Baig@hamza_baig894

The development of a Prompt Scoring Engine aims to enhance the output quality of Large Language Models (LLMs) and improve user experience. This engine evaluates and scores prompts based on various criteria to optimize the responses generated by LLMs. The implementation of this technology addresses key issues related to the effectiveness of AI models, the clarity of project presentations, business value, and originality. The project began with an extensive research phase, focusing on methods to evaluate prompts and develop criteria for their assessment. A rubric was created to score prompts from 1 (worst) to 7 (best). Following this, a configuration file for a custom GPT model was developed, which utilized the rubric to evaluate prompts and their inputs. This configuration provided a structured approach to identify areas for improvement and generate requirements for enhancing prompt quality. The core implementation involved creating a scoring engine that interacts with users by prompting them to input their original prompts and resulting outputs. The engine evaluates these inputs, scores them based on the established rubric, and suggests improvements. Users are then given the opportunity to try the new, optimized prompts and evaluate the results, creating a continuous feedback loop that refines the prompt quality over time. The Prompt Scoring Engine offers significant benefits, including enhanced output quality through advanced natural language processing techniques, improved user experience with more relevant and coherent responses, and a transparent, objective evaluation framework. Additionally, it serves as an educational tool, helping users understand and craft better prompts. This strategic initiative not only addresses critical issues in AI model effectiveness but also drives innovation and growth in the AI market by ensuring high-quality, original, and clear outputs.

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