LLM Engineering with RAG and Langfuse: A Beginner's Guide

Are you looking to build reliable and robust applications powered by large language models (LLMs)? Understanding how to integrate external knowledge and monitor your LLM interactions is crucial for creating effective and production-ready systems. This course will equip you with the foundational knowledge and practical skills to develop and manage Retrieval Augmented Generation (RAG) applications using Langfuse, an essential open-source platform for LLM engineering. By the end, you will be able to design, implement, and evaluate RAG workflows, ensuring your LLM applications are accurate and performant. What you'll learn: * Understand the core principles and architecture of Retrieval Augmented Generation (RAG) systems. * Set up and integrate Langfuse into your LLM application development workflow for observability. * Apply fundamental prompt engineering techniques to optimize RAG query effectiveness. * Evaluate the performance and reliability of your RAG pipelines using Langfuse tracing and analytics. * Identify and debug common issues in RAG applications to improve response quality. * Practice building simple RAG applications that leverage external data sources. The course begins with an introduction to LLMs and the necessity of RAG, then guides you through setting up Langfuse and progressively building and refining RAG applications with practical examples. This program is designed for absolute beginners with no prior experience in LLM engineering or RAG. All you need is a basic understanding of programming concepts and a desire to learn. Start your journey into building intelligent, data-driven LLM applications today.