Langfuse Tracing in Python: Turn LLM Failures into Eval Tests
About this lesson
Turn production failures into repeatable evals by capturing failing LLM runs with Langfuse in Python. Learn how to record structured traces, assign severity, promote failures into a dataset, and replay regressions as deterministic tests for CI. Demonstration uses the Langfuse Python SDK for tracing, scoring, dataset creation, and replayable evals to improve LLM observability and triage. Subscribe for practical AI engineering tutorials on regression testing, observability, and production LLM workflows. #Langfuse #Python #LLMops #AIEngineering #Observability #RegressionTesting #MachineLearning
Original Description
Turn production failures into repeatable evals by capturing failing LLM runs with Langfuse in Python.
Learn how to record structured traces, assign severity, promote failures into a dataset, and replay regressions as deterministic tests for CI.
Demonstration uses the Langfuse Python SDK for tracing, scoring, dataset creation, and replayable evals to improve LLM observability and triage.
Subscribe for practical AI engineering tutorials on regression testing, observability, and production LLM workflows.
#Langfuse #Python #LLMops #AIEngineering #Observability #RegressionTesting #MachineLearning
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
More on: LLMOps
View skill →Related Reads
📰
📰
📰
📰
Transform Business Operations with Robotic Process Automation (RPA) Services
Medium · AI
I Didn't Expect an AI Tutor to Beat My Favorite Online Course (But It Changed How I Learn)
Dev.to AI
How to Talk to Your Database Using AI: A Practical Implementation Guide
Dev.to · Erwin Wilson Ceniza2
ROGup Explained (4/4): The ROGin AI Token That Powers It All
Medium · AI
🎓
Tutor Explanation
DeepCamp AI