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In diesem Kurs gibt es 1 Modul
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.
Das ist alles enthalten
5 Videos3 Lektüren3 Aufgaben2 Unbewertete Labore
Infos zu Modulinhalt anzeigen
5 Videos•Insgesamt 29 Minuten
Why Logs Matter: The Air Canada Case?•6 Minuten
Calculating Retention in Pandas•6 Minuten
Why RAG Fails: The Root of Hallucination?•6 Minuten
Correlating Errors with Retrieval in Pandas•6 Minuten
Visualizing the Proof in Matplotlib•5 Minuten
3 Lektüren•Insgesamt 28 Minuten
Anatomy of a Log File•8 Minuten
The Engineering Brief: From Analysis to Action•10 Minuten
Authoring the Engineering Brief•10 Minuten
3 Aufgaben•Insgesamt 40 Minuten
Final Project: LLM Diagnostics Report•30 Minuten
Knowledge Check: Retention Metrics•5 Minuten
Knowledge Check: Communicating Findings•5 Minuten
2 Unbewertete Labore•Insgesamt 120 Minuten
Lab 1: Segmenting Users & Finding the Drop•60 Minuten
Lab 2: Proving the Root Cause•60 Minuten
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