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The article discusses prompt optimization as a key technique in event-driven development, mentioning frameworks like MLflow that support algorithms such as DSPy, GEPA, and MIProv2 to improve LLM prompts.
The article describes using Databricks Genie to query MLflow agent traces with natural language, enabling non-SQL users to analyze agent performance metrics like latency and errors.