Module 1: Introduction
Building an LLM-based prototype is straightforward today—you can connect to an API, send a few prompts, and get jaw-dropping outputs almost instantly. But bringing that prototype into production, where real people rely on it, is a different story. Performance bottlenecks, unpredictable latencies, unforeseen costs, and potential security risks can quickly escalate when an LLM application starts handling real user inputs at scale.
That’s why we built Langfuse—to give teams the observability, evaluation, and steering tools they need to confidently launch and maintain LLM applications in production. And it’s also why we wrote this guide: to share the best practices and practical workflows that will help you turn your impressive prototype into a reliable, cost-efficient service that delivers real value to your users.
Who Is This Course For?
This course is designed for developers and product teams who are already experimenting with Large Language Models (LLMs) or running small prototypes and want to level‑up to production‑grade LLM applications. If you use Langfuse—or plan to—this guide will give you the conceptual depth and practical skills to get from a prototype to a production-ready application.
What Is LLMOps?
LLMOps is the emerging discipline that adapts MLOps practices to the unique challenges of building LLM applications. It covers everything from:
- Tracing & observability to debug complex, often asynchronous chains of calls
- Evaluation & benchmarking (automated and human‑in‑the‑loop)
- Prompt design & management
- Cost & latency monitoring
- Continuous improvement through fine‑tuning and prompt iteration
In short, LLMOps is the toolkit that turns an impressive demo into a production service that is reliable, safe, and cost‑efficient.
Why is it important?
LLMOps is critical in turning LLM prototypes—easy enough to spin up with a few lines of code—into robust, production-grade applications that real users can rely on. By bringing observability, evaluation, and prompt management under one cohesive framework, LLMOps enables teams to monitor token usage, debug hidden latencies, and systematically refine prompts and workflows.
This operational layer also helps maintain cost control, ensures that unexpected bugs or “hallucinations” don’t slip into production, and keeps engineering, product, and data teams aligned on a single source of truth. In short, LLMOps is the bedrock for moving beyond proof-of-concept hacks to a sustainable, enterprise-scale service that can handle real-world demands.
Next, in Module 3, we’ll dive deeper into how tracing works, focusing on best practices for multi-step LLM apps and more advanced AI-agent workflows.
Core Pillars We’ll Dive Into
| Pillar | Why It Matters | Hands‑On Outcomes |
|---|---|---|
| Tracing | Understand every step of an LLM pipeline, from user input to nested tool calls, in order to debug latency, errors, and hallucinations. | You’ll instrument a multi‑tool agent and visualise traces in Langfuse. |
| Evaluation | Measure quality with automated metrics (e.g., factuality, toxicity) and human feedback before and after each release. | You’ll build custom evaluators and set up pass/fail gates in CI. |
| Prompt Management | Version prompts like code, collaborate safely, and link each prompt version to business KPIs. | You’ll store prompts in Langfuse and roll back a poor‑performing version. |
| Security | Mitigating risks introduced by powerful non-deterministic language models | You’ll set up guardrails to test your application online and offline |
| Enterprise Architecture | Robust applications require proven setups (failovers, rate limiting, etc.) | You’ll see how some of the best companies design their AI application architecture |
In Module 2, we’ll map out common application and agent architectures. That groundwork sets the stage for our tracing deep-dive in Module 3.