4A Consulting

Exam Generator POC for Productionization and Operations & Management

poc

Leading Humanitarian Organization Objective:Streamline the creation of dynamic educational assessments using an AI-powered system. The solution allowed users to upload textbook materials, define parameters (e.g., question type, complexity), and generate contextually accurate questions. The Proof of Concept (POC) successfully demonstrated the system’s potential, leading to its redevelopment for production with enhanced scalability and modularity. Scope […]

poc

Leading Humanitarian Organization

POC Development

Designed and developed a RAG-based proof of concept for generating dynamic questions from textbooks.

System Development

Redesigned the system for production, focusing on scalability and performance optimization.

Frontend Development

  • Created an intuitive interface using Streamlit for initial testing.
  • Rebuilt the frontend with React for a responsive and modern user experience.

Backend Optimizationiciency

Enhanced performance and modularity using FastAPI.

Containerization & Deployment

Containerized the system with Docker and deployed it on Kubernetes for efficient orchestration and resource scaling.

Guardrails Implementation

Integrated robust guardrails to ensure content accuracy, grounding, and appropriateness for generated questions.

Support and Monitoring

Established CI/CD pipelines for seamless updates and monitoring to maintain system quality and performance.

Managing Large Datasets from Textbooks

  • Implemented advanced chunking strategies to index large datasets effectively.
  • Optimized retrieval mechanisms to improve accuracy and efficiency.

Ensuring Accurate, Grounded, and Contextually Appropriate Question Generation

  • Developed guardrails to prevent hallucinations and ensure alignment with source materials.
  • Implemented checks for content appropriateness and contextual relevance.

Scaling the System for Multiple Users and Large Deployments

  • Adopted a microservices architecture to enable independent scaling and maintainability.
  • Containerized the application with Docker and deployed it on Kubernetes for high availability.

Maintaining System Relevance and Ensuring Updates

  • Utilized CI/CD pipelines to enable continuous updates to models and templates.

icon cloud

POC to Production Transition

  • Optimized retrieval mechanisms with chunking strategies for large-scale datasets.
  • Improved backend performance and modularity using FastAPI.

Solution Custom

Microservices Architecture

  • Separated question generation logic and business rules into distinct services for better scalability and maintainability.

icon code

Frontend Upgrade

  • Rebuilt the frontend with React to enhance user experience and meet modern standards.

icon backend

Deployment Scaling

  • Used Kubernetes and Docker to manage resources efficiently and handle increased demand.

icon

Continuous Improvement

  • Implemented CI/CD pipelines for ongoing updates, ensuring system relevance and optimal performance.

icon code

Generative AI and Integration

Retrieval-Augmented Generation (RAG), Azure OpenAI Model

icon

Workflow Management

PromptFlow

icon code

User Interface

Streamlit, React

icon backend

Backend Optimization

FastAPI

icon

Infrastructure and Deployment

Docker, Kubernetes, CI/CD Pipeline

Aspect Before After
Question Generation Manual, time-intensive Automated, efficient, and accurate
Scalability Limited Seamlessly scaled for high demand
User Interface Basic functionality (Streamlit) Modern and responsive (React)
Deployment Limited to small-scale testing Production-ready with Docker and Kubernetes

Reduced time for creating assessments by automating question generation.

Supported large-scale deployments with a scalable microservices architecture.

Improved user experience with a modernized interface and optimized backend.

Ensured high-quality outputs through robust guardrails.

Improved Efficiency

Automated question generation reduced the time and effort required for creating assessments.

Enhanced Scalability

The production system handled increased user demand without performance degradation.

.

 

Better User Experience

A modernized interface and optimized backend provided a smoother, faster experience.

Increased Accuracy

Robust guardrails ensured contextually relevant, high-quality outputs.

Quality Control

Prevented inappropriate or incorrect content generation, maintaining high standards for educational materials.

Conclusion

The Exam Generator system demonstrated the power of Retrieval-Augmented Generation (RAG) in creating dynamic, contextually accurate assessments. By leveraging advanced AI techniques, containerization, and scalable architecture, the solution significantly improved efficiency, scalability, and user experience. The production system not only met the immediate needs of educators but also provided a foundation for continued innovation in AI-driven assessment tools.

Let us help transform your assessment creation process with scalable, AI-driven solutions today!

Slider 3

    Area of Interest

    By submitting this form, you agree to the following:

    This will close in 0 seconds