From Prompt to Insight: LLM Tracking via LangFuse

Hosted by Aki Wijesundara, PhD

Fri, Nov 14, 2025

10:00 PM UTC (45 minutes)

Virtual (Zoom)

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AI Engineering Bootcamp & Certificate

Aki Wijesundara, PhD and Manu Jayawardana

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Fri, Nov 14, 2025

10:00 PM UTC (45 minutes)

Virtual (Zoom)

Free to join

Invite your network

Go deeper with a course

AI Engineering Bootcamp & Certificate

Aki Wijesundara, PhD and Manu Jayawardana

View syllabus

What you'll learn

Trace and Debug Any LLM Pipeline

Learn how to use LangFuse to visualize prompt flows, token usage, and performance so you can debug & optimize LLM apps.

Measure What Matters: Evals for Quality

Use structured evals to score responses, catch regressions, and track progress - Just like OpenAI and Anthropic.

Close the Feedback Loop

Use LangFuse to merge feedback, judge scores, and data into prompt iterations - turning intuition into measurable gains.

Why this topic matters

AI products don’t fail because of bad ideas they fail because of invisible errors and unmeasured quality. LangFuse bridges the gap between what you think your AI does and what it actually does in production. In this Lightning Lesson, you’ll master the practical workflow for tracing, evaluating, and continuously improving your LLM applications using the same open-source.

You'll learn from

Aki Wijesundara, PhD

AI Founder & Educator | Google AI Accelerator Alum

Aki Wijesundara is an AI leader with a PhD in Machine Learning and extensive experience mentoring startups at Google’s AI Accelerator. With a career spanning both research and applied AI, Aki has taught 5,000+ students worldwide how to design and deploy production-ready AI systems.

He has worked across cutting-edge areas of applied AI, from LangChain and RAG pipelines to observability and large-scale deployment. As a researcher and educator, Aki bridges the gap between theory and practice, making complex systems approachable and actionable for engineers, founders, and product leaders.

Aki is also a frequent speaker and advisor to organizations adopting AI, helping them transition from experimentation to production at scale.

Previously at