RAG Evaluation Course | CodersArts AI

Description

Your RAG system is running. Answers are coming back. But do you actually know if it's working — or are you just hoping it is? Most developers skip evaluation entirely. And that's exactly when silent failures slip through to production — wrong answers that look right, retrieved chunks that don't actually support the output, and pipelines nobody knows how to debug. This course changes that. ---- 🎓 RAG Evaluation — Measure, Diagnose & Improve Your RAG Pipeline 📅 Duration: 3 Weeks 💻 Format: Online | Hands-On 🔗 Enrol Now → ai.codersarts.com/courses --- In this course, you'll build a complete RAG evaluation framework from the ground up — learning how to measure what's actually happening at every stage of your pipeline, pinpoint exactly where failures occur, and make targeted improvements that actually hold up. --- 📌 WHAT YOU'LL LEARN ✅ Why RAG evaluation is hard — and why standard metrics miss the real failures ✅ How to build a golden dataset that makes your evaluation repeatable and meaningful ✅ Retrieval quality metrics — Recall@k, MRR, NDCG, and context relevance scoring ✅ Generation quality evaluation — faithfulness, groundedness, and answer relevance ✅ End-to-end evaluation workflows you can run consistently in production ✅ LLM-as-a-Judge — automating quality checks at scale using structured Pydantic outputs ✅ How to build a complete, working RAG Evaluation Pipeline in the capstone project --- 📚 COURSE MODULES Module 1 → Why RAG Evaluation Is Hard Module 2 → Building Your Golden Dataset Module 3 → Evaluating Retrieval Quality Module 4 → Evaluating Generation Quality Module 5 → End-to-End Evaluation Workflows Module 6 → Ranking Metrics and Test Data Module 7 → LLM as a Judge Module 8 → Capstone: Complete RAG Evaluation Pipeline Each module includes a hands-on lab with real code and real RAG outputs. --- 🛠️ TOOLS YOU'LL USE → OpenAI API → Python → Pydantic No complex setup. No toy examples. Just practical, production-ready skills. --- 👨‍💻 WHO THIS IS FOR → Developers who've built a RAG system and want to verify it actually works → AI/ML engineers responsible for pipeline quality in production → Professionals building evaluation workflows for LLM-powered products → Students learning systematic, rigorous AI quality assurance --- 💡 WHY THIS MATTERS A RAG system can produce an answer that sounds completely correct — while being factually unsupported by the retrieved context. Without structured evaluation, you'll never catch that before your users do. This course gives you the framework to stop guessing and start knowing. --- 🚀 ENROL NOW 👉 ai.codersarts.com/courses --- ⏱️ TIMESTAMPS 0:00 — Introduction 0:45 — Why RAG Evaluation Is Non-Negotiable 1:30 — Who This Course Is For 2:15 — What You'll Learn 3:00 — Course Modules Walkthrough 4:10 — Tools & Practical Value 4:50 — Enrol Now --- 🔔 STAY CONNECTED Subscribe for more hands-on AI engineering courses from CodersArts AI. 📌 Follow us → ai.codersarts.com 📧 Questions? Reach out via the website. --- #RAGEvaluation #RAG #LLM #AIEngineering #MachineLearning #GenerativeAI #PythonAI #LLMops #VectorDatabase #OpenAI #Pydantic #ProductionAI #CodersArtsAI #AIcourse #DeepLearning #RetrievalAugmentedGeneration #AIevaluation #MLops --- 📲Follow us on our Social Media Handles : ================================= 🔗 Main Website: https://www.codersarts.com/ 📚 Codersarts Training: https://www.training.codersarts.com/ 🤖 Codersarts AI: https://www.ai.codersarts.com/ 📷 Instagram: https://www.instagram.com/codersarts/?hl=en 📘 Facebook: https://www.facebook.com/codersarts2017 💼 LinkedIn: https://in.linkedin.com/company/codersarts #Codersarts #AI #ML #Upskilling #POCs #MVPs