#16 LLM Evaluation & Monitoring Explained: Golden Sets, SLAs & Drift Detection

Description

📊 Evaluation & Monitoring for LLMs (Golden Sets, SLAs & Drift) How do you trust an LLM in production? In this video, we break down evaluation and monitoring strategies used in real-world LLM, RAG, and Agentic AI systems—covering golden datasets, SLAs, and model drift detection. If you’re deploying chatbots, copilots, or autonomous agents, this video will help you measure quality, catch failures early, and scale safely. 🚀 What You’ll Learn ✅ What Golden Sets are and how to build them ✅ Offline vs Online LLM Evaluation ✅ Defining SLAs & SLOs for LLM systems ✅ Monitoring latency, cost, accuracy, hallucination rate ✅ Detecting data drift, prompt drift & model drift ✅ Evaluation strategies for RAG pipelines ✅ Monitoring Agentic workflows & tool calls ✅ Production-grade evaluation architecture 🧠 Topics Covered LLM Evaluation Metrics Golden Dataset Creation Human vs LLM-as-Judge Evaluation Retrieval Quality Monitoring (RAG) Drift Detection Techniques Feedback Loops & Continuous Improvement Alerts, Dashboards & Observability 🏗️ Real-World Use Cases Chatbots & Virtual Assistants Enterprise RAG Systems Autonomous Agent Pipelines Customer Support AI Internal Knowledge Assistants 👍 Like | Share | Subscribe If this helped you understand LLM evaluation & monitoring, hit 👍 and subscribe for more Agentic AI & RAG deep dives #ArtificialIntelligence #MachineLearning #DeepLearning #AIYouTube #TechEducation #AIExplained #ProductionAI #AIMonitoring #MLMonitoring #MLSystems #ScalableAI #EnterpriseAI #ResponsibleAI #LLMEvaluation #LLMMonitoring #LLMInProduction #AIProduction #aiengineering #GoldenDataset #GoldenSet #SLA #SLO #ModelDrift #DataDrift #PromptDrift #DriftDetection #MLOps #AIObservability #ModelMonitoring #RAG #AgenticAI #AutonomousAgents #AIPipelines #VectorDatabases #RetrievalAugmentedGeneration #LLMAgents