Quality and Safety for LLM Applications

What You'll Learn

• Monitor and enhance security measures to protect your LLM applications over time.
• Detect and prevent critical security threats like hallucinations, jailbreaks, and data leakage.
• Explore real-world scenarios to better prepare for potential risks and vulnerabilities.

About This Course

This course focuses on addressing and monitoring safety and quality concerns specific to LLM applications, which present unique security challenges. You’ll learn best practices, metrics, and hands-on techniques for improving the safety and quality of your applications.

• Identify hallucinations with methods like SelfCheckGPT.
• Detect jailbreaks and mitigate manipulative prompts using sentiment analysis and toxicity detection.
• Identify and prevent data leakage using entity recognition and vector similarity.
• Build a monitoring system to continuously evaluate the safety and security of your application.

By the end of this course, you will be well-equipped to identify and address common security concerns in LLM-based applications, customizing your safety evaluation tools to suit the LLM in use.

Course Outline

• Introduction
  Introduction to safety and quality considerations in LLM applications.
• Overview
  General overview of monitoring systems and best practices for secure LLM applications.
• Hallucinations
  Techniques to detect and mitigate hallucinations using methods like SelfCheckGPT.
• Data Leakage
  Identifying and preventing data leakage with entity recognition and vector similarity analysis.
• Refusals and Prompt Injections
  Managing prompt injections and refusals through sentiment analysis and toxicity detection.
• Passive and Active Monitoring
  Implementing both passive and active monitoring systems to maintain application security over time.
• Conclusion
  Recap of safety practices and key takeaways for securing LLM applications.

Who Should Join?

This course is ideal for anyone with basic Python knowledge who is interested in mitigating issues like hallucinations, prompt injections, and toxic outputs in LLM applications.