Building Secure and Trustworthy LLMs Using NVIDIA Guardrails

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Nayan Saxena

56:49

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01 - Introduction

01 - Leverage guardrails to build secure LLMs.mp4

01:08

02 - What you should know.mp4

01:02

03 - Exercise files and setting up your environment.mp4

01:43

02 - 1. Foundations of Guardrails for Language Models

01 - Introduction to guardrails for LLMs.mp4

01:55

02 - Behind the scenes How Guardrails enforces LLM safety.mp4

04:35

03 - Understanding the role of embeddings in safeguarding LLMs.mp4

03:10

03 - 2. Crafting Conversational Guardrails

01 - Defining conversational boundaries.mp4

02:52

02 - Advanced Colang flows.mp4

04:34

03 - Guardrails in action.mp4

02:42

04 - 3. Integrating Custom Actions

01 - Enhancing LLMs with custom actions.mp4

03:40

02 - Building a custom inquiry action.mp4

03:20

05 - 4. Retrieval Augmented Generation (RAG) with Guardrails

01 - Retrieval-augmented generation (RAG) with actions.mp4

05:09

02 - Securing LLMs against sensitive topics.mp4

04:01

03 - Debugging and optimizing Guardrails.mp4

04:18

06 - 5. Real-World Applications and Best Practices

01 - Case studies Guardrails in action.mp4

02:49

02 - Best practices for implementing guardrails.mp4

05:23

03 - Future of guardrails and LLM safety.mp4

02:46

07 - Conclusion

01 - Next steps in building LLM applications.mp4

01:42

Description

Guardrails are essential components of large language models (LLMs) that can help to safeguard against misuse, define conversational standards, and enhance public trust in AI technologies. In this course, instructor Nayan Saxena explores the importance of ethical AI deployment to understand how NVIDIA NeMo Guardrails enforces LLM safety and integrity. Learn how to construct conversational guidelines using Colang, leverage advanced functionalities to craft dynamic LLM interactions, augment LLM capabilities with custom actions, and elevate response quality and contextual accuracy with retrieval-augmented generation (RAG). By witnessing guardrails in action and analyzing real-world case studies, you'll also acquire skills and best practices for implementing secure, user-centric AI systems. This course is ideal for AI practitioners, developers, and ethical technology advocates seeking to advance their knowledge in LLM safety, ethics, and application design for responsible AI.

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Machine Learning

Nayan Saxena

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.