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Currently Playing: Langfuse vs LangSmith Explained | LLM Observability for RAG & AI Agents

Learn Langfuse vs LangSmith and understand LLM Observability from scratch with a beginner-friendly, practical explanation. In this video, we explore why traditional application monitoring is not enough for Generative AI applications and how LLM observability helps us understand what is really happening inside RAG pipelines, LLM applications, and AI Agents. We start with the fundamentals of LLM observability and then explain Langfuse and LangSmith step by step using a simple RAG application. You will understand how both platforms help track prompts, retrieval, LLM calls, tools, latency, tokens, cost, traces, observations, runs, evaluations, and agent execution. Topics Covered: • What is LLM Observability? • Why traditional monitoring fails for AI applications • What should we monitor inside an LLM application? • RAG and AI Agent observability architecture • Langfuse explained from scratch • Langfuse Session, Trace, Observation, and Generation • Langfuse Retriever, Tool, Event, and Agent observations • RAG execution flow using Langfuse • LangSmith explained from scratch • LangSmith Thread, Trace, Run, and Trajectory • RAG execution flow using LangSmith • Langfuse vs LangSmith terminology mapping • Trace vs Observation vs Run • Prompt management and versioning • Token usage, latency, and API cost tracking • LLM evaluation and LLM-as-a-Judge • Debugging RAG and Agentic AI applications • Langfuse vs LangSmith similarities and differences Key Terminology: Langfuse: Session → Trace → Observation → Generation LangSmith: Thread → Trace → Run → Model Run This tutorial is useful for: • Beginners learning Generative AI • AI/ML Engineers • GenAI Developers • AI Architects • RAG Developers • Agentic AI Engineers • LangChain and LangGraph Developers • MLOps and LLMOps Engineers • Cloud Engineers • DevOps Engineers • Developers preparing for AI/ML interviews About Naveen TechHub: Naveen TechHub provides beginner-friendly, concept-first, and practical technology tutorials designed to connect fundamentals with real-world engineering. Topics include: • Generative AI • Agentic AI • Large Language Models • RAG • LangChain • LangGraph • AI Agents • LLM Observability • LLMOps and MLOps • AI/ML Engineering • System Design • AWS and Azure • DevOps • Docker • Kubernetes • CI/CD • GitHub Actions • Jenkins • Infrastructure Automation The goal is simple: understand the concept first, then see how it works in a real engineering architecture. Subscribe to Naveen TechHub for more practical and beginner-friendly AI, Cloud, DevOps, and Software Engineering tutorials. #Langfuse #LangSmith #LLMObservability #GenerativeAI #RAG #AgenticAI #AIagents #LLM #LangChain #LangGraph #LLMOps #MLOps #AIEngineering #GenAI #NaveenTechHub