<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Thamu Mnyulwa: Machine Learning Engineer — Agentic Craft</title><description>Practical notes on AI agents, machine learning systems, and building useful software.</description><link>https://thamu.dev/</link><language>en</language><item><title>GenAI Evaluation with mlflow.genai.evaluate(): Beyond Accuracy</title><link>https://thamu.dev/blog/genai-evaluation-with-mlflow-genai-evaluate-beyond-accuracy/</link><guid isPermaLink="true">https://thamu.dev/blog/genai-evaluation-with-mlflow-genai-evaluate-beyond-accuracy/</guid><description>How to evaluate RAG and agentic GenAI systems with MLflow 3.x, mlflow.genai.evaluate(), LLM-as-a-judge scorers, custom trace-aware metrics, and prompt A/B tests.</description><pubDate>Tue, 14 Jul 2026 05:41:36 GMT</pubDate><category>mlflow</category><category>generative ai</category><category>#llmops</category><category>RAG </category><category>Evaluation</category><category>Prompt Engineering</category><category>mlops</category></item><item><title>Productionising Generative AI with MLflow 3.x: Tracing, Evaluation, and Prompt Optimisation</title><link>https://thamu.dev/blog/productionising-generative-ai-mlflow-3x-tracing-evaluation-prompt-optimisation/</link><guid isPermaLink="true">https://thamu.dev/blog/productionising-generative-ai-mlflow-3x-tracing-evaluation-prompt-optimisation/</guid><description>How MLflow 3.x helps productionise GenAI systems with OpenTelemetry-compatible traces, trace-aware evaluation, RAG judges, custom scorers, and prompt registry workflows.</description><pubDate>Tue, 14 Jul 2026 00:20:02 GMT</pubDate><category>mlflow</category><category>generative ai</category><category>#llmops</category><category>RAG </category><category>observability</category><category>mlops</category></item></channel></rss>