## Overview The Education and Training Quality Authority (BQA) is a governmental body in Bahrain responsible for overseeing and improving the quality of education and training services across the country. BQA reviews the performance of schools, universities, and vocational institutes as part of a comprehensive quality assurance process. This case study describes a proof of concept (PoC) solution developed in collaboration with AWS through the Cloud Innovation Center (CIC) program, a joint initiative involving AWS, Tamkeen, and Bahraini universities. It is important to note that this is explicitly a proof of concept rather than a fully deployed production system, so the claimed benefits should be understood as anticipated rather than proven outcomes. The core problem BQA faced was the labor-intensive and error-prone nature of reviewing self-evaluation reports (SERs) submitted by educational institutions. Institutions were required to submit documentation and supporting evidence as part of the review process, but submissions frequently contained incomplete or inaccurate information, lacked sufficient supporting evidence to substantiate claims, and required significant manual follow-up to rectify issues. This created bottlenecks in the overall review workflow and consumed substantial organizational resources. ## Technical Architecture The solution leverages multiple AWS services orchestrated together to create an intelligent document processing pipeline. The architecture follows a serverless, event-driven pattern that enables scalable document handling: **Document Ingestion and Queuing**: Documents are uploaded to Amazon S3, which triggers event notifications sent to Amazon SQS queues. SQS serves as a decoupling layer between processing stages, providing reliability and fault tolerance. This buffering approach is a common pattern in production LLM systems where document processing can be variable in duration and resource consumption. **Text Extraction**: AWS Lambda functions are invoked by the SQS queue to process documents using Amazon Textract for text extraction. Textract handles the OCR and structured data extraction from various document formats, converting them into machine-readable text that can be processed by the LLM components. **Text Summarization**: Extracted text is placed into another SQS queue for summarization. A separate Lambda function sends requests to SageMaker JumpStart, where a Meta Llama text generation model is deployed. This model summarizes content based on provided prompts, condensing lengthy submissions into concise formats that reviewers can quickly assess. **Compliance Assessment**: In parallel with summarization, another Lambda function invokes the LLM to compare extracted text against BQA standards. The model evaluates submissions for compliance, quality, and other relevant metrics against the criteria the model was trained or prompted with. **Storage and Retrieval**: Summarized data and assessment results are stored in Amazon DynamoDB, providing a queryable database of processed submissions. **Generative AI Evaluation**: Upon request, an additional Lambda function invokes Amazon Bedrock using the Amazon Titan Text Express model to generate detailed summaries and compliance comments for reviewers. ## LLM Selection and Usage The solution employs two distinct LLM components for different purposes. Amazon Titan Text Express, accessed through Amazon Bedrock, serves as the primary model for generating compliance evaluations and detailed feedback. Amazon Bedrock is described as a fully managed service providing access to foundation models through a unified API, which simplifies model deployment and management compared to self-hosted solutions. Additionally, SageMaker JumpStart hosts a Meta Llama model for text summarization tasks. This dual-model approach suggests the team evaluated different models for different tasks and found that specialized deployment made sense for their use case. However, the documentation does not provide detailed rationale for why these specific models were chosen or how their performance compared to alternatives. ## Prompt Engineering Approach The case study provides concrete examples of the prompt engineering techniques employed, which is valuable for understanding how the LLM is guided to produce structured, useful outputs. The prompt template includes several key components: **Context Provision**: The prompt presents the evidence submitted by the institution under the relevant indicator, giving the model the necessary context for evaluation. This follows the pattern of providing domain-specific context to guide model behavior. **Evaluation Criteria Specification**: The prompt outlines the specific rubric criteria against which evidence should be assessed. By embedding these standards directly in the prompt, the system doesn't require fine-tuning the model on BQA-specific standards. **Explicit Instructions**: The prompt includes detailed instructions for handling edge cases (indicating N/A for irrelevant evidence), the scoring methodology (a three-tier compliance scale: Non-compliant, Compliant with recommendation, Compliant), and response formatting requirements (concise bullet points, 100-word limit). **Model Parameters**: The implementation sets specific generation parameters including maxTokenCount of 4096, temperature of 0 (for deterministic outputs), and topP of 0.1 (very focused sampling). These conservative parameters suggest the team prioritized consistency and reproducibility over creative variation in outputs. The prompt engineering approach demonstrates several best practices: providing clear context, specifying expected output format, including explicit edge case handling, and constraining response length. This structured approach helps ensure consistent, actionable outputs from the LLM. ## Infrastructure Considerations The serverless architecture using Lambda functions and SQS queues represents a pragmatic approach for a proof of concept. This pattern offers automatic scaling, pay-per-use pricing, and reduced operational overhead compared to managing dedicated compute resources. For document processing workloads that may be bursty (such as during submission deadlines), this elasticity is particularly valuable. The use of DynamoDB for storing processed results provides a managed NoSQL database solution that integrates well with the Lambda-based architecture. This allows for quick retrieval of assessment results and supports the real-time feedback capabilities mentioned as a benefit of the system. ## Claimed Results and Caveats The case study presents several anticipated success metrics, but it's crucial to note that these are projected benefits from a proof of concept rather than measured outcomes from production deployment: - 70% accuracy for standards-compliant self-evaluation report generation - 30% reduction in evidence analysis time - 30% reduced operational costs through process optimizations - Faster turnaround times for report generation These metrics should be treated with appropriate skepticism. The 70% accuracy figure is notably modest for an automated compliance system, suggesting the team has realistic expectations about LLM capabilities for this task. The 30% reduction figures are common claims in automation case studies but would need validation through actual deployment to be considered reliable. The case study also highlights qualitative benefits including enhanced transparency in communications, real-time feedback enabling prompt adjustments, and data-driven insights for institutional improvement. These softer benefits are harder to measure but represent meaningful process improvements if realized. ## Limitations and Considerations Several limitations should be noted when evaluating this case study. First, this is explicitly a proof of concept developed through an educational innovation program, not a battle-tested production system. The involvement of university students through the CIC program suggests this may be more experimental than enterprise-grade. Second, the case study is published by AWS and focuses primarily on showcasing AWS services. The technical depth regarding failure modes, edge cases, error handling, and quality assurance processes is limited. Real production deployments would need robust mechanisms for handling cases where the LLM produces incorrect or inappropriate outputs. Third, there's no discussion of how the system handles documents in Arabic (Bahrain's primary language), which would be a significant consideration for actual deployment. Multilingual document processing introduces additional complexity not addressed in this overview. Finally, the integration between human reviewers and the AI system is not deeply explored. Understanding how BQA staff interact with and validate AI-generated assessments would be important for assessing the practical utility of this solution. ## Conclusion This case study illustrates a common pattern in government adoption of generative AI: using managed cloud services and LLMs to automate document processing workflows that were previously manual and time-consuming. The architecture demonstrates sound serverless design principles and the prompt engineering approach shows thoughtful consideration of how to guide LLM outputs. However, as a proof of concept, the claimed benefits remain theoretical until validated through actual deployment and measurement. Organizations considering similar implementations should note the experimental nature of this project and plan for appropriate evaluation and iteration before full production deployment.