This case study examines how BQA (Education and Training Quality Authority) in Bahrain implemented a production LLM system to revolutionize their education quality assessment process. The implementation showcases a comprehensive approach to deploying LLMs in a production environment, with particular attention to document processing, evaluation, and compliance checking workflows. ### Overview and Context BQA is responsible for evaluating and ensuring the quality of education across all educational institutions in Bahrain. Their traditional process involved manually reviewing self-evaluation reports (SERs) and supporting documentation from educational institutions, which was time-consuming and prone to inconsistencies. The challenge was particularly complex due to the need to process various document formats while ensuring compliance with established standards. ### Technical Architecture and Implementation The solution architecture demonstrates a well-thought-out approach to LLMOps, incorporating several key components: * **Document Processing Pipeline**: The system uses Amazon S3 for document storage, with event notifications triggering processing through Amazon SQS queues. This architecture ensures reliable and scalable document handling in production. * **Text Extraction Layer**: A dedicated Lambda function uses Amazon Textract to extract text from various document formats, showing how LLM systems can be integrated with other AI services for comprehensive document processing. * **Dual Model Approach**: The solution uniquely combines two different LLM models: * Meta Llama model (via SageMaker JumpStart) for text summarization * Amazon Titan Express model (via Amazon Bedrock) for evaluation and compliance checking * **Data Storage and Management**: The system uses DynamoDB for storing processed results, enabling efficient retrieval and analysis of evaluations. ### Prompt Engineering Strategy The implementation showcases sophisticated prompt engineering practices essential for production LLM systems: * **Structured Prompting**: The team developed a carefully structured prompt template that guides the model to: * Analyze evidence against specific indicators * Evaluate compliance with institutional standards * Generate numerical scores (1-5 scale) * Provide justification based on direct evidence * **Output Control**: The prompts include specific constraints: * Word limits (100 words) for responses * Structured output format requirements * Clear scoring criteria (non-compliant, compliant with recommendation, compliant) ### Production Considerations The implementation addresses several critical aspects of running LLMs in production: * **Scalability**: The use of SQS queues and Lambda functions ensures the system can handle varying loads of document processing requests. * **Error Handling**: The architecture includes mechanisms for handling missing or insufficient evidence, with automated feedback loops for requesting additional information. * **Quality Control**: The system maintains quality through: * Temperature settings of 0 for consistent outputs * Top-P settings of 0.1 for focused responses * Maximum token count limits to prevent runaway generations ### Integration and Workflow The solution demonstrates sophisticated integration patterns: * **Event-Driven Architecture**: The use of SQS queues and Lambda functions creates a robust, event-driven system capable of handling asynchronous document processing at scale. * **Service Orchestration**: The system coordinates multiple AWS services effectively, showing how LLMs can be part of a larger production ecosystem. * **Feedback Loops**: Real-time feedback mechanisms allow for continuous improvement of submissions and evaluations. ### Results and Performance Metrics The implementation achieved significant measurable improvements: * 70% accuracy in generating standards-compliant reports * 30% reduction in evidence analysis time * 30% reduction in operational costs * Improved compliance feedback functionality * Enhanced transparency and communication in the review process ### Lessons and Best Practices The case study highlights several important LLMOps best practices: * **Model Selection**: The use of different models for different tasks (summarization vs. evaluation) shows thoughtful model selection based on specific requirements. * **Prompt Design**: The structured approach to prompt engineering demonstrates the importance of careful prompt design in production systems. * **System Architecture**: The event-driven, queue-based architecture shows how to build resilient, scalable LLM systems. * **Integration Patterns**: The solution demonstrates effective patterns for integrating LLMs with existing business processes and other AI services. ### Future Considerations The implementation sets the stage for future enhancements: * Potential for expanding the system to handle more complex document types * Opportunities for implementing more sophisticated feedback loops * Possibilities for adding more advanced analytics and reporting capabilities This case study serves as an excellent example of how organizations can effectively implement LLMs in production for complex document processing and evaluation tasks, while maintaining high standards of accuracy and efficiency.