How You Catch Production Hallucinations in Real Time

Lightning Lessons

Practical skills & tools to accelerate your career

How You Catch Production Hallucinations in Real Time

Hosted by Jason Liu and Julia Neagu

Wed, Jul 30, 2025

5:00 PM UTC (1 hour)

Virtual (Zoom)

Free to join

Learn directly from undefined

By continuing, you agree to Maven's Terms and Privacy Policy.

Invite your network

Go deeper with a course

Systematically Improving RAG Applications

Jason Liu

View syllabus

Wed, Jul 30, 2025

5:00 PM UTC (1 hour)

Virtual (Zoom)

Free to join

Invite your network

Go deeper with a course

Systematically Improving RAG Applications

Jason Liu

View syllabus

What you'll learn

Build real-time hallucination detectors

Learn to implement monitoring systems that catch AI errors as they happen in live production environments

Debug production AI agent failures

Understand how to identify and troubleshoot broken agent behavior using real-world debugging techniques

Deploy monitoring with minimal code

Master adding detection systems to existing AI workflows using just a few lines of practical code

Why this topic matters

Production AI systems fail silently—hallucinating facts, missing key information, or breaking entirely. Without real-time monitoring, these failures reach users and damage trust. This lesson teaches you to catch problems before customers do, building the reliability skills that separate junior developers from senior engineers who ship production-ready AI.

You'll learn from

Jason Liu

Consultant at the intersection of Information Retrieval and AI

Jason has built search and recommendation systems for the past 6 years. He has consulted and advised a dozens startups in the last year to improve their RAG systems. He is the creator of the Instructor Python library.

Julia Neagu

Co-founder & CEO, Quotient AI

Julia is the co-founder and CEO of Quotient AI, building the critical monitoring layer for AI agents in production. Before founding Quotient, Julia was the Director of Data for GitHub Copilot, where her team built the systems behind one of the world’s most widely used AI coding assistants. She previously served as Director of Analytics at Tamr and built quantitative models for Aon’s Intellectual Property Solutions group. Julia holds a PhD and MA in Physics from Harvard and an AB in Physics from Princeton.

worked with