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Analyze Logs: Fix LLM Hallucinations

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Analyze Logs: Fix LLM Hallucinations

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Use data analysis to diagnose LLM hallucinations by correlating user behavior and system errors, and document findings to guide engineering fixes.

Details to know

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Recently updated!

December 2025

Assessments

3 assignmentsΒΉ

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the LLM Optimization & Evaluation Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There is 1 module in this course

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.

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.

What's included

5 videos3 readings3 assignments2 ungraded labs

5 videosβ€’Total 29 minutes
  • Why Logs Matter: The Air Canada Case?β€’6 minutes
  • Calculating Retention in Pandasβ€’6 minutes
  • Why RAG Fails: The Root of Hallucination?β€’6 minutes
  • Correlating Errors with Retrieval in Pandasβ€’6 minutes
  • Visualizing the Proof in Matplotlibβ€’5 minutes
3 readingsβ€’Total 28 minutes
  • Anatomy of a Log Fileβ€’8 minutes
  • The Engineering Brief: From Analysis to Actionβ€’10 minutes
  • Authoring the Engineering Briefβ€’10 minutes
3 assignmentsβ€’Total 40 minutes
  • Final Project: LLM Diagnostics Reportβ€’30 minutes
  • Knowledge Check: Retention Metricsβ€’5 minutes
  • Knowledge Check: Communicating Findingsβ€’5 minutes
2 ungraded labsβ€’Total 120 minutes
  • Lab 1: Segmenting Users & Finding the Dropβ€’60 minutes
  • Lab 2: Proving the Root Causeβ€’60 minutes

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276 Coursesβ€’32,516 learners

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