Evaluating RAG Systems & Continual Learning – Metrics & Feedback (GenAI Course – 7)

Description

This is **GenAI Course – Video 7** on VectorMind Academy. In this final Week 1 video, we give a **summary-level view** of how to **evaluate RAG applications** and think about **continual learning**. 🎯 Goal of this video We are not coding a full evaluation framework. By the end, you should roughly know: • Why evaluation is critical for LLM / RAG systems • The difference between manual and automated evaluation • Example metrics: relevance, correctness, latency, cost • How feedback loops and continual learning improve your system over time • Where MLflow, Vector Search and monitoring tools connect to evaluation 💡 How to use this video • Use it as a checklist of “what should I measure?” for your GenAI app • Pick 2–3 metrics that matter most for your use case • Then explore Databricks and MLflow docs for concrete evaluation workflows 🔁 Week 1 – Generative AI Solution Development (summary series) 1️⃣ Prompt Engineering Primer 2️⃣ Introduction to RAG 3️⃣ Preparing Data for RAG Solutions 4️⃣ Introduction to Vector Stores 5️⃣ Mosaic AI Vector Search 6️⃣ MLflow for RAG Applications 7️⃣ Evaluating a RAG Application & Continual Learning ✅ (this video) If this summary helped you understand how to judge and improve RAG systems, like the video, subscribe, and stay tuned for future GenAI & Data content on **VectorMind Academy**. 🚀 #rag #llm #generativeai #modelevaluation #mlops #databricks #aicourse #VectorMindAcademy