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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:

1. Identify hallucinations with methods like SelfCheckGPT.

2. Detect jailbreaks (prompts that attempt to manipulate LLM responses) using sentiment analysis and implicit toxicity detection models.

3. Identify data leakage using entity recognition and vector similarity analysis.

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:

1. Identify hallucinations with methods like SelfCheckGPT.

2. Detect jailbreaks (prompts that attempt to manipulate LLM responses) using sentiment analysis and implicit toxicity detection models.

3. Identify data leakage using entity recognition and vector similarity analysis.

4. 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.

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What's inside

Syllabus

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Read about what's good

what should give you pause

and possible dealbreakers

Explores common security concerns in LLM applications

Teaches new metrics to monitor LLM systems

Helps you identify hallucinations with methods like SelfCheckGPT

Teaches sentiment analysis and implicit toxicity detection models

Helps build a monitoring system to evaluate app safety over time

Taught by Bernease Herman, an expert in LLM application safety

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Reviews summary

Ensuring llm quality and safety

According to learners, this course provides actionable strategies and a practical, hands-on approach to ensuring quality and safety in LLM applications. Students frequently highlight the clear explanations of complex topics like hallucination detection (SelfCheckGPT), jailbreak prevention, and data leakage identification. Many found the labs and the process of building a monitoring system particularly useful. While it offers highly relevant and current industry insights, some students suggest a prior understanding of Python and machine learning concepts is beneficial for optimal pacing.

Provides a solid foundation, but not deep dives into every specific vulnerability.

"It's good for an overview, but I'll need to do more self-study to truly grasp everything."

"Don't expect deep dives into every single vulnerability."

"I wish there was a bit more on prompt engineering security best practices beyond just jailbreaks."

Well-structured content with clear explanations from the instructor.

"Instructor explanations were clear and concise, making complex topics digestible."

"Labs are well-structured."

"The instructor clearly knows their stuff."

Course content is timely and highly relevant to current industry needs.

"The course is very current, which is a huge plus in this fast-moving field."

"Highly relevant for current industry challenges."

"This was an excellent and timely course."

Comprehensive and clear explanation of critical LLM safety issues.

"The SelfCheckGPT and vector similarity analysis modules were highlights."

"The concepts of jailbreak detection using sentiment analysis were well-explained."

"The data leakage section was insightful, especially the entity recognition part."

"It covers the core safety aspects for LLMs."

Provides immediately applicable tools and hands-on experience.

"The practical examples for detecting hallucinations with SelfCheckGPT were invaluable."

"It gave me practical tools to immediately apply to my work."

"As an ML engineer, this course provided exactly what I needed: actionable strategies for LLM quality and safety."

"The hands-on labs where we built a basic monitoring system were particularly useful for me."

Pace can be fast, benefiting those with existing ML/Python skills.

"This course is definitely for those with some Python and ML background, not for absolute beginners."

"I found the pace a bit fast in places, especially if I wasn't already comfortable with certain ML concepts."

"Felt like it assumed too much prior knowledge."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Quality and Safety for LLM Applications with these activities:

Identify Mentors with LLM Expertise

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Connect with experienced professionals to receive personalized guidance and support in navigating the complexities of LLM safety and quality.

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Network with individuals in the field through conferences, meetups, or online forums.
Seek out mentors with specific expertise in LLM safety and quality assurance.

LLM Safety Study Group

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Engage in peer-led discussions and collaborative problem-solving to reinforce safety and quality practices in LLM applications.

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Join or form a study group with peers to discuss and share experiences in LLM safety and quality assessment.
Present and critique each other's approaches for monitoring and mitigating LLM risks.
Brainstorm innovative solutions to emerging challenges in LLM safety and security.

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Career center

Learners who complete Quality and Safety for LLM Applications will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 25 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Quality and Safety for LLM Applications.

Sentiment Analysis

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Considered a key book specifically focused on sentiment analysis, this resource offers a comprehensive introduction from an NLP perspective. It covers core areas, including opinion mining, and has been updated to include recent deep learning methods. is valuable for researchers and practitioners and serves as a great introductory and reference book.

Quality and Safety for LLM Applications

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What's inside

Syllabus

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Reviews summary

Ensuring llm quality and safety

Activities

Career center

Reading list

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