Building Secure and Trustworthy LLMs Using NVIDIA Guardrails

سرفصل های دوره

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.

01 - Introduction

01 - Leverage guardrails to build secure LLMs

02 - What you should know

03 - Exercise files and setting up your environment

02 - 1. Foundations of Guardrails for Language Models

01 - Introduction to guardrails for LLMs

02 - Behind the scenes How Guardrails enforces LLM safety

03 - Understanding the role of embeddings in safeguarding LLMs

03 - 2. Crafting Conversational Guardrails

01 - Defining conversational boundaries

02 - Advanced Colang flows

03 - Guardrails in action

04 - 3. Integrating Custom Actions

01 - Enhancing LLMs with custom actions

02 - Building a custom inquiry action

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

01 - Retrieval-augmented generation (RAG) with actions

02 - Securing LLMs against sensitive topics

03 - Debugging and optimizing Guardrails

06 - 5. Real-World Applications and Best Practices

01 - Case studies Guardrails in action

02 - Best practices for implementing guardrails

03 - Future of guardrails and LLM safety

07 - Conclusion

01 - Next steps in building LLM applications

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تولید کننده: LinkedIn Learning (Lynda)