Uber has developed a sophisticated enterprise-scale prompt engineering toolkit that demonstrates a mature approach to managing LLMs in production. This case study provides valuable insights into how a major technology company handles the complexities of LLM operations at scale, offering a comprehensive look at the full lifecycle of prompt engineering and LLM deployment. The toolkit was developed to address several key challenges in enterprise LLM deployment: * The need for centralized prompt template management * Support for rapid iteration and experimentation * Integration of RAG capabilities * Robust evaluation frameworks * Production-grade deployment and monitoring * Version control and collaboration features The architecture of the system is particularly noteworthy for its comprehensive approach to LLMOps. The toolkit is built around several key components that work together to provide a complete solution: The development stage of their prompt engineering lifecycle shows careful consideration of the experimentation process. Users begin with a Model Catalog that contains detailed information about available LLMs, including metrics and usage guides. This is complemented by a GenAI Playground that allows for interactive exploration and testing. This approach demonstrates good practices in model governance and discovery, making it easier for teams to select the appropriate models for their use cases. A particularly innovative aspect is their approach to prompt template management. The system includes automated prompt generation capabilities built on top of their internal Langfx framework (based on LangChain). The prompt builder incorporates various advanced prompting techniques including: * Chain of Thought (CoT) * Automatic Chain of Thought * Prompt chaining * Tree of Thoughts (ToT) * Automatic prompt engineering * Multimodal CoT prompting The version control and deployment system shows careful consideration of production safety. They've implemented a sophisticated revision control system that follows code-based iteration best practices, requiring code reviews for prompt template changes. The deployment system includes safety features like explicit deployment tagging and configuration synchronization through their ObjectConfig system, preventing accidental changes in production. The evaluation framework is particularly robust, supporting both automated and human-in-the-loop approaches: * LLM-as-judge evaluation for subjective quality assessment * Custom code-based evaluation for specific metrics * Support for both golden datasets and production traffic * Aggregated metrics for comparing different prompt templates For production deployment, the system supports both offline batch processing and online serving scenarios. The offline batch processing pipeline is particularly noteworthy, supporting large-scale LLM response generation with features like dynamic template hydration. This is demonstrated in their rider name validation use case, where they process large volumes of usernames for legitimacy verification. The online serving capabilities include sophisticated features like: * Dynamic placeholder substitution using Jinja-based template syntax * Fan-out capabilities across prompts, templates, and models * Support for different API templates (chat completion, text completion) * Integration with their existing infrastructure Real-world applications demonstrate the system's practical utility. For example, in their customer support use case, they use the system to generate summaries of support tickets during agent handoffs, improving operational efficiency. This shows how LLMs can be practically integrated into existing business processes. The monitoring system is comprehensive, tracking various metrics including latency, accuracy, and correctness. They run daily performance monitoring pipelines against production traffic, with results displayed in their MES dashboard. This demonstrates good practices in continuous monitoring and quality assurance. However, there are some limitations and areas for improvement that they acknowledge. The system currently only handles string type substitutions in templates, and they plan to evolve the toolkit to better integrate with online evaluation and RAG systems. The case study is particularly valuable for organizations looking to implement LLMOps at scale, as it provides a comprehensive view of how to handle the full lifecycle of LLM deployment in a production environment. The attention to safety, version control, and evaluation is especially noteworthy, showing how enterprise-grade LLM systems can be built and maintained effectively.