## Overview This case study from Microsoft's ISE (Industry Solutions Engineering) team describes a customer engagement with a leading retailer to build an LLM-powered system for analyzing customer feedback at scale. The retailer was receiving tens of thousands of feedback comments daily, making manual review impractical. Traditional NLP models were found to be ineffective, particularly for handling lengthy review comments. The solution leveraged Azure OpenAI with GPT models to extract themes, sentiment, and competitor comparisons from shopper feedback, enabling the business to make data-driven decisions to improve customer satisfaction. It's worth noting that this blog post is somewhat prescriptive and represents a reference architecture rather than a fully detailed production deployment. The authors acknowledge that evaluation of LLM outputs is critical before rolling out to production, though the specifics of evaluation techniques are not covered in detail. ## Architecture and Data Pipeline The high-level architecture centers on a data pipeline that ingests customer feedback from various data sources, performs data cleansing and enrichment, and then calls Azure OpenAI to generate insights. The core module, referred to as the "Themes Extraction and Sentiment Generator," handles the LLM interactions. The data pipeline approach reflects LLMOps best practices by treating LLM calls as part of a broader data processing workflow. Key components include: - **Data ingestion** from source systems where customer feedback is collected - **Data cleansing** to remove meaningless comments (e.g., "Nil", "Null", "No comments") and one-word responses - **Data enrichment** by prefixing category information to comments when available from the feedback collection tool - **Schema validation** to ensure data quality and prevent hallucinations or exceptions during LLM inference - **LLM inference** via Azure OpenAI GPT models - **Output processing** for reporting and visualization in tools like Microsoft Power BI The emphasis on data preparation before LLM calls is a practical LLMOps consideration that many organizations overlook. Garbage in, garbage out applies strongly to LLM applications. ## Prompt Engineering Approach A significant portion of the case study focuses on iterative prompt engineering, which is presented as the primary technique for achieving reliable, production-quality outputs. The authors describe an evolutionary approach to prompt development: ### Starting Simple The initial prompt was basic: "Extract all the themes mentioned in the provided feedback and for each of the themes generate the sentiment." Running this simple prompt repeatedly revealed inconsistency problems—the same underlying concepts would be extracted as different themes across runs (e.g., "Cleanliness" vs. "Neatness" vs. "Tidiness"). This lack of consistency made the outputs unsuitable for analytics purposes like trend analysis. ### Introducing Predefined Themes To address inconsistency, the team developed a predefined list of themes through an iterative process: repeatedly running the basic prompt across feedback from different time periods, manually reviewing and grouping similar themes, then selecting the most appropriate representative theme from each group. Subject matter experts from the retail domain contributed to finalizing this list. The predefined themes for this retail use case include: - Checkout Experience - Insufficient Staff - Product Availability - Product Pricing - Product Quality - Product Variety - Shopping Experience - Store Cleanliness - Store Layout By constraining the LLM to select from this predefined list, the system produces consistent, comparable outputs suitable for aggregation and trend analysis. ### Structured Output Format The prompts explicitly request JSON-formatted output, which is essential for downstream processing in production systems. The system prompt establishes the AI's role, the expected input format, the task requirements, and the output format. This structured approach makes the outputs programmatically consumable. ### Progressive Prompt Enhancement The case study demonstrates how prompts can be incrementally enhanced to extract additional information: First, theme extraction was added with sentiment classification (positive, negative, or neutral). Then, competitor comparison extraction was layered on, asking the model to identify mentions of competing retailers and the sentiment associated with those comparisons. This progressive refinement approach is valuable for LLMOps practitioners who need to balance prompt complexity with reliability. ## Production Considerations The blog touches on several important LLMOps considerations for production deployment: ### Evaluation Before Production The authors explicitly note that evaluation of LLM outputs using different evaluation techniques is critical before the solution can be rolled out to production. While the specifics aren't covered, this acknowledgment reflects mature thinking about LLMOps practices. The gap between a working prototype and a production-ready system often lies in rigorous evaluation. ### Responsible AI and Data Privacy The case study emphasizes masking sensitive data in reviews before sending to the LLM, ensuring the solution follows Responsible AI principles. This is a critical consideration for any production LLM deployment, particularly when dealing with customer-generated content that may contain personally identifiable information. ### Consistency and Reliability The discussion of inconsistent theme generation highlights a fundamental challenge in LLM production systems: non-deterministic outputs. The solution of using predefined theme categories is a practical constraint-based approach to improve reliability, though it does limit the system's ability to discover new, emergent themes. ### Hierarchical Aggregation The system is designed to generate insights at the store level that can be aggregated up through district, state, regional, and national levels. This hierarchical reporting structure requires consistent, structured outputs—reinforcing the importance of the constrained prompt approach. ## Reporting and Visualization The LLM outputs feed into various reporting capabilities: - Sentiment visualization graphs showing positive and negative trends - Theme impact analysis identifying which factors most influence sentiment - Weekly trend reports to validate the effectiveness of actions taken - Drill-down features allowing users to see actual customer comments from sentiment graphs - Hierarchical reports for store managers and business managers at various organizational levels Integration with Microsoft Power BI is mentioned, suggesting a typical enterprise analytics workflow where LLM-generated insights are consumed through existing business intelligence infrastructure. ## Limitations and Balanced Assessment While the case study presents a useful reference architecture, several limitations should be noted: - **Lack of quantitative results**: No metrics are provided on accuracy, precision, recall, or cost of the solution. It's unclear how well the system actually performs in practice. - **Evaluation details absent**: While evaluation is mentioned as important, no specific techniques or results are shared. - **Hallucination mitigation**: The approach of using predefined themes addresses consistency but doesn't fully address hallucination risks. The model could still misclassify themes or generate incorrect sentiment labels. - **Scalability and cost**: Processing tens of thousands of daily feedbacks through GPT models has cost implications that aren't discussed. - **Latency considerations**: No discussion of response times or batch vs. real-time processing trade-offs. The architecture is presented at a conceptual level, and practitioners would need to fill in significant details around error handling, retry logic, rate limiting, model versioning, monitoring, and other operational concerns. ## Key Takeaways for LLMOps Practitioners Despite its limitations, the case study offers several practical insights: - **Data preparation matters**: Investing in data cleansing and enrichment before LLM calls improves output quality. - **Constrain for consistency**: Using predefined categories or options helps produce consistent, analytically useful outputs. - **Iterate on prompts**: Start simple and evolve prompts based on observed behavior, not assumptions. - **Involve domain experts**: Subject matter experts are valuable for defining appropriate categories and validating outputs. - **Design for integration**: Structure outputs (e.g., JSON) to facilitate downstream processing and reporting. - **Plan for evaluation**: Build evaluation into the development process before production deployment. - **Consider responsible AI**: Address data privacy and sensitive information handling from the design phase. This case study represents a practical, if somewhat high-level, guide to building LLM-powered feedback analysis systems in retail contexts, with patterns applicable to other industries receiving customer feedback.