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DeepLearning.AI

Bernease Herman

In this course, you will explore new metrics and best practices to monitor your LLM systems and ensure safety and quality. You will learn to identify hallucinations, detect jailbreaks, and identify data leakage. Additionally, you will build your own monitoring system to evaluate app safety and security over time. By the end of the course, you will be able to identify common security concerns in LLM-based applications and customize your safety and security evaluation tools.

Quality and Safety for LLM Applications

Overview of the importance of safety and quality in LLM applications.

Introduction to LLM Safety and Quality

Methods like SelfCheckGPT to identify hallucinations in LLM responses.

Identifying Hallucinations

Using sentiment analysis and implicit toxicity detection models to detect jailbreaks.

Detecting Jailbreaks

Techniques like entity recognition and vector similarity analysis to identify data leakage.

Identifying Data Leakage

How to build your own monitoring system to evaluate app safety and security over time.

Building a Monitoring System

This course addresses the critical safety and quality concerns in LLM applications. Learn to monitor and enhance security measures, detect and prevent threats, and explore real-world scenarios to safeguard your LLM applications.

Anyone with basic Python knowledge interested in mitigating issues like hallucinations, prompt injections, and toxic outputs.

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Quality and Safety for LLM Applications

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