LinkedIn's implementation of LLMs for automated search quality evaluation represents a significant case study in practical LLMOps deployment. The company faced the challenge of evaluating typeahead suggestions quality for their search system that serves over 1 billion members, where traditional human evaluation methods were becoming unsustainable due to scale. The case study demonstrates several key aspects of successful LLM deployment in production: **System Architecture and Integration** LinkedIn chose to implement their solution using OpenAI's GPT models served through Azure, showing a practical approach to enterprise LLM deployment. The system was designed with clear evaluation pipelines that integrate with their existing typeahead backend. This architecture allows for systematic batch processing of quality evaluations and seamless integration with their experimentation framework. **Prompt Engineering and Quality Control** The team developed a sophisticated prompt engineering approach with several noteworthy characteristics: * Structured prompt templates with clear sections: IDENTITY, TASK GUIDELINES, EXAMPLES, INPUT, and OUTPUT * Specialized evaluation prompts for different result types (People entities, Job suggestions, etc.) * Implementation of chain-of-thought reasoning by requiring the LLM to explain its scoring decisions * Careful calibration through multiple iterations and cross-validation with human evaluators **Data Management and Testing** A crucial aspect of the implementation was the creation of a golden test set for evaluation. This demonstrated several best practices: * Comprehensive coverage of different search intents with 200 queries per category * Inclusion of both successful (clicked) and unsuccessful (bypassed/abandoned) sessions * Consideration of user demographics through member lifecycle data * Careful sampling methodology to ensure representative evaluation **Quality Metrics and Evaluation** The team implemented a sophisticated evaluation framework: * Binary scoring system (1 for high-quality, 0 for low-quality) to reduce ambiguity * Multiple quality metrics (TyahQuality1, TyahQuality3, TyahQuality5, TyahQuality10) to evaluate different aspects of the ranking * Clear guidelines for different suggestion types to ensure consistent evaluation **Production Deployment Considerations** The system was designed with several production-ready features: * Batch processing capabilities for efficient evaluation * Integration with existing experimentation infrastructure * Clear evaluation pipelines with defined steps for processing new experiments * Ability to handle different types of suggestions and user contexts **Results and Impact** The implementation showed significant benefits: * Reduced evaluation time from weeks to hours * Demonstrated measurable improvements in suggestion quality * Enabled rapid experimentation and validation of new features * Provided consistent, objective quality measurements at scale **Challenges and Learnings** The team encountered and addressed several challenges: * Dealing with the inherent complexity of typeahead suggestions * Managing the subjective nature of relevance evaluation * Handling personalization aspects of suggestions * Iterating on prompt design to improve accuracy **Risk Management and Quality Assurance** The implementation included several risk mitigation strategies: * Cross-evaluation of GPT outputs against human evaluation * Multiple iterations of prompt refinement * Clear quality guidelines to reduce ambiguity * Comprehensive testing across different suggestion types and use cases **Future Considerations** The case study suggests several areas for potential improvement: * Further refinement of prompt engineering * Expansion to other search quality evaluation use cases * Integration with other types of automated testing * Potential for more sophisticated evaluation metrics This case study demonstrates a practical, production-ready implementation of LLMs for automation of a critical quality evaluation task. It shows how careful system design, prompt engineering, and integration with existing systems can create significant value in a production environment. The implementation successfully balanced the need for accuracy with the requirement for scalability, while maintaining clear evaluation criteria and metrics. The approach taken by LinkedIn provides valuable insights for other organizations looking to implement LLMs for automated evaluation tasks, particularly in showing how to maintain quality while scaling up evaluation capabilities. The careful attention to prompt engineering, test set creation, and evaluation metrics provides a template for similar implementations in other contexts.