Welcome to Langfuse Academy
Building with LLMs changes what it means for a system to work. Outputs are probabilistic. A system can run fine and still produce responses that are wrong, off-brand, or useless. Teams need to reason about quality, cost, latency, and the tradeoffs between them.
Langfuse Academy maps the AI engineering lifecycle so you understand how the pieces fit and what it takes to ship from prototype to production.
What you will find here
The Langfuse Academy follows the AI engineering lifecycle from first visibility into production behavior all the way to structured improvement and evaluation. The goal is to explain why each step exists, what problem it solves, and how the steps connect.
Start with The AI Engineering Loop for the high-level map, then go deeper into the individual parts:
Some pages explain the high-level concepts. Others are deeper dives into individual parts of the lifecycle. You can read the full sequence or jump to the topic that is most relevant to your team right now.
Why we are publishing this
Langfuse is open source, and we want to open source the conceptual side of AI engineering too. The Academy is our way of making the core ideas, vocabulary, and workflows behind LLM application development easier to access for everyone.
Who this is for
- AI engineers and software engineers building LLM applications and agentic systems
- Product managers who need to reason about quality, iteration, and tradeoffs
- Technical and business leaders who need a working understanding of how AI systems are built and improved
- AI agents that support humans in understanding AI engineering concepts and workflows