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.
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Analyze Logs: Fix LLM Hallucinations
本课程是 LLM Optimization & Evaluation 专项课程 的一部分
位教师：LearningMate
包含在 中
您将学到什么
Use data analysis to diagnose LLM hallucinations by correlating user behavior and system errors, and document findings to guide engineering fixes.
您将获得的技能
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有1个模块
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.
涵盖的内容
5个视频3篇阅读材料3个作业2个非评分实验室
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位教师
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状态：免费DeepLearning.AI
状态：免费DeepLearning.AI
状态：免费试用
状态：免费试用
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