ScholarDen faced challenges in generating new test questions efficiently. Dedicated human resources focused on crafting sentences, which were subsequently scrutinized by another team to sift through and identify useful content. This process demanded significant time and manpower, impacting overall productivity and turnaround times for creating high-quality test questions.
The Challenge
ScholarDen faced challenges in generating new test questions efficiently. Dedicated human resources focused on crafting sentences, which were subsequently scrutinized by another team to sift through and identify useful content. This process demanded significant time and manpower, impacting overall productivity and turnaround times for creating high-quality test questions.
How Did We Take on the Challenge?
Veritas Analytica addressed ScholarDen’s challenges by conducting a thorough analysis of their existing question-generation process. We developed a comprehensive strategy that included curating a diverse and high-quality dataset, implementing customizable prompt inputs, and refining the model’s output through continuous fine-tuning. Additionally, we created a streamlined application that integrated all these components seamlessly, enhancing usability and efficiency.
The Roadblocks
Acquiring a Comprehensive Dataset: Needed a diverse and high-quality dataset suitable for training the Language Model (LLM).
Implementing Customizable Prompt Inputs: Designed an effective mechanism for users to input customizable prompts.
Refining Output Through Fine-Tuning: Achieved optimal performance through continuous refinement and adjustment of various parameters.
Developing a Streamlined Application: Integrated multiple components into a single, user-friendly application.
Features of Our Solution
Automated Processes
Enhanced efficiency and productivity with a user interface application.
Customizable Dataset
Offered runtime dataset modification, segregated according to the difficulty level.
Default Prompt Modification
Featured a robust prompt modification button, allowing users to set prompts according to their needs.
Rapid Response
Generated up to 50 questions with a response time of less than a minute.
Tech Stack Used
Meta Llama 3
Python
Solution
Curated Dataset Acquisition
Gathered a comprehensive dataset of questions tailored for the model’s training needs.
Interactive Prompt Customization
Provided intuitive customization options through an interactive application interface.
Output Optimization
Refined the model’s outputs to ensure high-quality results.
Unified Application Integration
Integrated all processes into a single application, enabling users to perform every task efficiently with a single click.
Impact
The application had the capacity to generate up to 50 questions, with an initial user acceptance rate of up to 60%.
Data Flow Diagram
Client Testimonial
Client Name Designation of Client
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Date: 07/24/2024
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