Quality and Safety for LLM Applications

It’s always crucial to address and monitor safety and quality concerns in your applications. Building LLM applications poses special challenges.

In this course, you’ll explore new metrics and best practices to monitor your LLM systems and ensure safety and quality. You’ll learn how to: 

• Identify hallucinations with methods like SelfCheckGPT 
• Detect jailbreaks (prompts that attempt to manipulate LLM responses) using sentiment analysis and implicit toxicity detection models.
• Identify data leakage using entity recognition and vector similarity analysis.
• Build your own monitoring system to evaluate app safety and security over time.

Upon completing the course, you’ll have the ability to identify common security concerns in LLM-based applications, and be able to customize your safety and security evaluation tools to the LLM that you’re using for your application.

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