
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.
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.

POC to Production Transition
- Optimized retrieval mechanisms with chunking strategies for large-scale datasets.
- Improved backend performance and modularity using FastAPI.

Microservices Architecture
- Separated question generation logic and business rules into distinct services for better scalability and maintainability.

Frontend Upgrade
- Rebuilt the frontend with React to enhance user experience and meet modern standards.

Deployment Scaling
- Used Kubernetes and Docker to manage resources efficiently and handle increased demand.

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

Generative AI and Integration
Retrieval-Augmented Generation (RAG), Azure OpenAI Model

Workflow Management
PromptFlow

User Interface
Streamlit, React

Backend Optimization
FastAPI

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.
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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.
- Transitioned from a successful POC to a fully functional, scalable production system.
- Transitioned from a successful POC to a fully functional, scalable production system.
- Provided educators with a reliable, efficient tool to create high-quality assessments.
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!
