Analyze Logs: Fix LLM Hallucinations

Overview

When a production chatbot starts giving incorrect answers, how do you find the problem and fix it? "Analyze Logs: Fix LLM Hallucinations" is an intermediate course that equips AI practitioners, ML engineers, and data analysts with the essential skills for debugging production LLMs. Go beyond theory and learn the systematic, data-driven workflow that professionals use to solve the critical problem of AI hallucinations. You will utilize the Pandas library to analyze production logs, segment user behavior by intent, and calculate key business metrics, such as 7-day retention, to identify which user journeys are failing. Then, you will perform a root cause analysis, correlating different error types with retrieval system performance to pinpoint exactly why your model is failing. Finally, you will learn to translate your analytical findings into a clear, actionable engineering brief that drives real solutions. This course will empower you to transition from merely observing AI failures to expertly diagnosing and resolving them.

Syllabus

Production Log Analysis: From Retention to Root Cause

This module provides an end-to-end walkthrough of how to diagnose and address LLM hallucinations using production log data. You will start by calculating high-level business metrics, such as user retention. You will then dive deep to perform a root cause analysis, correlating model errors with system failures. Finally, you will learn to communicate your findings in a professional engineering brief.