Build Better LLM Apps
The AI Engineering Loop with Langfuse, end to end
A free 2-hour online workshop for engineers building production LLM applications.
LIVE Virtual Workshop - Zoom
Thursday, 30 July 2026
9:30 AM IST | 12:00 PM SGT | 2:00 PM AEST
Most teams shipping AI features today are iterating on instinct, tweaking prompts, swapping models, adjusting parameters, with no reliable way to know if the change actually made things better. That slow, blind cycle is the bottleneck between a prototype and a production-grade AI product.
This workshop gives you the tooling and workflow to close that loop. Walk away with working traces, managed prompts, and a repeatable eval workflow.
What you will build
Turn a blind app with no visibility into a functioning AI engineering loop. You'll work through a real TypeScript LLM agent — the Dad IT Support Agent — and instrument it from scratch using Langfuse.
By the end of the session you'll have:
Full trace visibility across every agent step
Prompts versioned and managed outside your codebase
A monitoring setup that catches out-of-scope requests automatically
A dataset + eval run you can reuse on any future change
Who is it for?
Engineers shipping LLM features and flying blind on what's actually happening in production
Tech leads evaluating observability tooling for their AI stack
Anyone who's asked: "How do I know if my prompt change made things better or worse?"
You should be comfortable reading TypeScript. No prior Langfuse experience needed.
What to prepare If you are interested in hands-on experience, the come prepared before the session:
Node.js 18+ installed
A Langfuse Cloud account (free) — sign up at cloud.langfuse.com
An OpenAI API key with available credits
The instructor will run through the labs with you - LIVE!
Agenda - 120 minutes
15 min: Introduction to Langfuse and the AI engineering loop
5 min: Langfuse Cloud walkthrough
10 min: Workshop environment setup
60 min: Hands-on labs — tracing, prompt management, monitoring, evals
15 min: AI engineering loop demo
15 min: Q&A and resources