LangFuse: LLM Observability, Tracing, Evaluation, Monitoring

About this course

A warm welcome to LangFuse for LLMOps: Observability, Tracing, Evaluation & Monitoring course by Uplatz.

LangFuse is an open-source LLM observability and evaluation platform that helps AI engineers trace, monitor, debug, evaluate, and optimize production-grade AI, RAG, and agentic applications.

Large Language Models (LLMs) have transformed the way modern applications are built, but deploying AI systems into production introduces new challenges around visibility, debugging, monitoring, evaluation, cost control, and reliability. Traditional monitoring tools are not designed to understand prompts, model responses, retrieval pipelines, agent workflows, or AI-specific performance metrics.

This course provides a comprehensive, hands-on introduction to LangFuse, one of the leading open-source platforms for LLM observability, tracing, prompt management, evaluation, and production monitoring. You will learn how to instrument AI applications, capture traces, analyze model behavior, monitor token usage and costs, evaluate response quality, and build reliable AI systems that can be confidently deployed at scale.

Starting from the fundamentals, you will explore LangFuse architecture, traces, spans, observations, sessions, and observability concepts before moving into practical implementation using the LangFuse SDK and OpenAI-compatible APIs. Through real-world examples, you will learn how to monitor LLM interactions, manage prompt versions, enrich traces with metadata, collect feedback, perform evaluations, and identify performance bottlenecks.

The course also covers advanced topics including observability for Retrieval-Augmented Generation (RAG) applications, agentic AI systems, production readiness, self-hosting strategies, CI/CD integration, testing workflows, and common implementation mistakes. Finally, you will build a complete mini-project where you instrument a real AI application an

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