Rechat's journey into implementing LLMs in production represents a valuable case study in the systematic development and deployment of AI agents in a business context. The company, which primarily serves real estate agents and brokers, leveraged their existing APIs and data to create an AI assistant that could handle complex real estate-related tasks. The case study demonstrates the evolution from prototype to production-ready system, highlighting key challenges and solutions in LLMOps implementation. Initially, Rechat built their prototype using GPT-3.5 and React framework. While the prototype showed promise when it worked, it suffered from consistency issues and slow performance. The team quickly realized that moving to production would require a more structured approach to evaluation and improvement. The key transformation came through the implementation of a comprehensive evaluation framework, which addressed several critical aspects of LLMOps: **Foundation: Unit Tests and Assertions** The team emphasized the importance of starting with basic unit tests and assertions, a step often overlooked in LLM applications. They created specific tests based on observed failure modes, such as checking for invalid placeholders, email sending issues, and unwanted repetitions. These tests were integrated into their CI pipeline, providing immediate feedback on basic functionality. **Logging and Human Review System** A crucial component of their LLMOps strategy was the implementation of robust logging and review systems. While they used LSmith for trace logging, they made the important decision to build their own custom data viewing and annotation tools. This choice was driven by the need to accommodate domain-specific requirements and reduce friction in the data review process. The custom tool facilitated efficient human review and data labeling, incorporating real estate-specific metadata and filtering capabilities. **Test Case Generation** To ensure comprehensive testing coverage, Rechat employed LLMs to synthetically generate test inputs by simulating real estate agent queries. This approach helped bootstrap their test cases before having actual user data, demonstrating an innovative use of LLMs in the testing process. **Iterative Improvement Process** The framework enabled a systematic approach to improvement through: * Continuous prompt engineering with measurable results * Data curation for fine-tuning * Automated filtering of good cases for review * Workflow management for handling failed cases * Integration of LLM-as-judge capabilities, carefully aligned with human judgment **Production Challenges and Solutions** The case study revealed several interesting insights about LLM deployment in production: * The limitations of few-shot prompting for complex use cases * The necessity of fine-tuning for handling mixed structured and unstructured output * The importance of managing user feedback and complex multi-step commands * The challenge of integrating UI elements within natural language responses One particularly notable achievement was the system's ability to handle complex commands requiring multiple tool interactions. For example, the AI agent could take a single complex request from a real estate agent and break it down into multiple function calls to create listings, generate websites, create Instagram posts, and send emails - all while maintaining context and proper sequencing. **Key Learnings and Best Practices** The case study emphasizes several important LLMOps principles: * Start with simple tools and existing infrastructure before adopting specialized solutions * Prioritize data visibility and reduce friction in data review processes * Avoid premature adoption of generic evaluations in favor of domain-specific tests * Ensure proper alignment between automated (LLM) and human evaluation * Maintain a balance between automation and human oversight The results showed significant improvements in reliability and capability, enabling the system to handle complex real estate workflows that would typically take hours to complete manually. The evaluation framework proved crucial in achieving production-ready performance and maintaining consistent quality across various use cases. The case study also highlights the importance of being realistic about current LLM capabilities. While there's often discussion about using simple prompting solutions, Rechat's experience shows that production-grade applications often require more sophisticated approaches combining fine-tuning, structured evaluation, and careful system design. This implementation demonstrates how a systematic approach to LLMOps, focusing on evaluation and continuous improvement, can transform a promising prototype into a reliable production system. The success of this approach is particularly evident in the system's ability to handle complex, multi-step tasks while maintaining reliability and user trust.