This case study explores Microsoft's implementation of an LLM-based system for analyzing customer feedback in a retail environment, demonstrating a practical application of LLMs in production. The solution addresses the common challenge of manually processing large volumes of customer feedback, which can be particularly time-consuming when dealing with tens of thousands of daily reviews. The architecture and implementation details reveal several important aspects of putting LLMs into production: **System Architecture and Data Pipeline** The solution is built around Azure OpenAI's GPT models and includes a comprehensive data pipeline. The architecture consists of several key components: * Data ingestion from various sources * Data preprocessing including cleansing and enrichment * A Themes Extraction and Sentiment Generator module that interfaces with OpenAI * Storage solutions using Azure Blob Storage * Visualization through Microsoft Power BI **Data Preprocessing and Quality** The implementation emphasizes the importance of data quality and preparation before feeding it to the LLM: * Data enrichment by adding contextual information from source data * Removal of meaningless one-word comments and null values * Schema validation to prevent LLM hallucinations * Data cleansing pipeline to ensure consistent input quality **Prompt Engineering Methodology** The case study provides detailed insights into their prompt engineering approach: * Iterative development starting with basic prompts * Evolution of prompts based on observed performance * Use of predefined themes to ensure consistency in output * Structured output formatting using JSON * Careful consideration of industry-specific requirements **Theme Extraction and Standardization** A notable aspect of the implementation is the approach to maintaining consistent theme extraction: * Initial broad theme generation * Manual review and grouping of similar themes * Creation of predefined theme lists specific to the retail industry * Domain expert involvement in theme selection and validation **Sentiment Analysis Integration** The system combines theme extraction with sentiment analysis: * Three-way sentiment classification (positive, negative, neutral) * Theme-specific sentiment extraction * Integration with competitor comparison analysis **Production Considerations** Several important production-ready features are implemented: * Handling of inconsistent outputs through predefined themes * Schema validation for reliable processing * Error handling for various input types * Scalability considerations for processing large volumes of feedback **Responsible AI Implementation** The case study emphasizes responsible AI practices: * Masking of sensitive data before LLM processing * Output evaluation before production deployment * Consideration of bias in theme extraction * Data privacy compliance **Output Processing and Reporting** The solution includes comprehensive reporting capabilities: * Generation of trend analysis graphs * Drill-down capabilities to original feedback * Hierarchical aggregation of insights (store to national level) * Integration with business intelligence tools **Limitations and Considerations** The case study acknowledges several important considerations: * The need for continuous prompt refinement * Importance of domain expertise in theme definition * Potential limitations in handling industry-specific terminology * Need for output evaluation before production deployment **Results and Business Impact** The implementation provides several business benefits: * Automated processing of large feedback volumes * Consistent theme categorization * Actionable insights at various organizational levels * Trend analysis capabilities * Competitive intelligence through automated competitor mention analysis **Technical Integration Details** The solution leverages several Microsoft technologies: * Azure OpenAI for LLM capabilities * Azure Blob Storage for data management * Power BI for visualization * Azure Machine Learning for model management The case study demonstrates a well-thought-out approach to implementing LLMs in a production environment, with careful consideration given to data quality, consistent output generation, and practical business value. The implementation shows how proper prompt engineering, combined with structured data processing and responsible AI practices, can create a reliable production system for automated insight generation from customer feedback.