--- title: Langfuse Academy description: Understand why LLM engineering is different and how to navigate the full AI engineering lifecycle. --- # Welcome to Langfuse Academy Building AI applications and agents is very different from traditional software. Outputs are probabilistic, and teams need to reason about quality, cost, latency, and the tradeoffs between them. Langfuse Academy explains the AI engineering lifecycle to help you understand how the pieces fit together and what it takes to ship from prototype to production. ## What you will find here The structure of the academy follows the AI engineering lifecycle, which is a continuous loop. It explains why each step exists, why, and how the steps connect. For each step, you can choose how deep you go. Start with [The AI Engineering Loop](/academy/ai-engineering-loop), or dive into an individual step right away: The AI Engineering Loop: - [Trace](/academy/tracing): traces, sessions, agents, prompts - [Monitor](/academy/monitoring): dashboards, LLM-as-judge, feedback - [Build datasets](/academy/datasets): datasets, features-as-tests - [Experiment](/academy/experiments): prompts, models, code variants - [Evaluate](/academy/evaluate): judges, custom evals, annotation - [Trace](/academy/tracing) - [Monitor](/academy/monitoring) - [Build datasets](/academy/datasets) - [Experiment](/academy/experiments) - [Evaluate](/academy/evaluate) 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. - 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 _The Academy is also [available in Japanese](/academy/japan)._ --- ## Agent Instructions This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/academy.md`. ### Querying these docs If the answer is not on this page, query the documentation instead of guessing: - **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question: ```bash curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?" ``` - **Index of every page**: , with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt). ### Before writing Langfuse code - **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results. - **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application. - **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples. - **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api `) to read or write traces, prompts, datasets, and scores from the terminal. Found an error in these docs? Please open an issue at .