AI Operations Under the Hood: Challenges and Best Practices

Building robust generative AI applications requires a practical framework for rigorous evaluation, safety, and dependable operations, as models alone are insufficient. The piece illustrates these challenges with a real-world example of an insurance contract analysis tool built using RAG, fine-tuning a Mixtral model, and prompt engineering. This system addresses the complexities of maintaining and improving AI products, a field often called LLMOps or GenAIOps. Key operational challenges include managing prompt drift, silent context retrieval failures, and evolving business logic, which necessitates a continuous lifecycle of monitoring and refinement that begins after initial deployment.