Anaconda's case study presents a comprehensive approach to deploying and improving LLMs in production through their Evaluations Driven Development (EDD) methodology. The study focuses on their AI coding assistant, demonstrating how systematic evaluation and refinement can transform a proof-of-concept AI tool into a reliable production system. The core challenge Anaconda faced was ensuring their AI assistant could consistently and accurately help data scientists with Python coding tasks, particularly debugging. Rather than simply deploying a language model with basic prompts, they developed a rigorous testing and evaluation framework called "llm-eval" to continuously measure and improve the assistant's performance. The technical implementation consists of several key components: * A comprehensive testing framework that simulates thousands of realistic user interactions * Systematic evaluation of model outputs across different scenarios * An automated execution environment for testing generated code * A feedback loop for continuous prompt refinement What makes this case study particularly valuable from an LLMOps perspective is the detailed performance data and methodology they share. The initial evaluation results were quite poor, with success rates of only 0-13% across different models and configurations: * GPT-3.5-Turbo (v0125) at temperature 0: 12% success * GPT-3.5-Turbo (v0125) at temperature 1: 13% success * Mistral 7B Instruct v0.2 at temperature 0: 0% success * Mistral 7B Instruct v0.2 at temperature 1: 2% success Through their EDD process, they achieved dramatic improvements: * GPT-3.5-Turbo (v0125) at temperature 0: improved to 87% * GPT-3.5-Turbo (v0125) at temperature 1: improved to 63% * Mistral 7B Instruct v0.2 at temperature 0.1: improved to 87% * Mistral 7B Instruct v0.2 at temperature 1: improved to 100% The case study provides valuable insights into their prompt engineering process. They employed several sophisticated techniques: * Few-shot learning with carefully selected examples from real-world Python errors * Chain-of-thought prompting to encourage step-by-step reasoning * Systematic prompt refinement based on evaluation results * An innovative "Agentic Feedback Iteration" process where they use LLMs to analyze and suggest improvements to their prompts One of the most interesting aspects from an LLMOps perspective is their "Agentic Feedback Iteration" system. This meta-use of LLMs to improve LLM prompts represents an innovative approach to prompt optimization. They feed evaluation results, including original prompts, queries, responses, and accuracy metrics, back into a language model to get specific suggestions for prompt improvements. The case study also highlights important considerations for production deployment: * The impact of temperature settings on model reliability * The importance of controlled execution environments for testing * The need for comprehensive evaluation across different model types * The value of detailed telemetry data in understanding user interaction patterns Their "llm-eval" framework demonstrates several LLMOps best practices: * Automated testing across thousands of scenarios * Controlled execution environments for safety * Systematic measurement of accuracy and performance * Continuous improvement through data-driven refinement Looking at the results critically, while the improvements are impressive, it's worth noting that the case study primarily focuses on Python debugging tasks. The generalizability of their approach to other domains would need to be validated. Additionally, while they mention telemetry data showing that 60% of user interactions involve debugging help, more detailed user success metrics would be valuable. From an infrastructure perspective, they're using both OpenAI's GPT-3.5-Turbo and locally deployed Mistral 7B models, suggesting a hybrid approach to model deployment. This provides flexibility and redundancy, though they don't go into detail about their deployment architecture. Their future roadmap includes promising LLMOps initiatives: * Expanding their evaluation framework to handle more complex scenarios * Open-sourcing the "llm-eval" framework * Incorporating user feedback into their improvement cycle * Supporting multi-step coding challenges * Adding domain-specific evaluation criteria This case study provides a valuable blueprint for organizations looking to deploy LLMs in production, particularly for developer tools and coding assistants. Their systematic approach to evaluation and improvement, backed by concrete metrics and a robust testing framework, demonstrates how to transform promising AI technology into reliable production systems.