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

Описание: 📊 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