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This is a repository for the LinkedIn Learning course Building Secure and Trustworthy LLMs Using NVIDIA Guardrails

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Building Secure and Trustworthy LLMs Using NVIDIA Guardrails

This is the repository for the LinkedIn Learning course Building Secure and Trustworthy LLMs Using NVIDIA Guardrails. The full course is available from LinkedIn Learning.

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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.

See the readme file in the main branch for updated instructions and information.

Instructions

This repository has branches for each of the videos in the course. You can use the branch pop up menu in github to switch to a specific branch and take a look at the course at that stage, or you can add /tree/BRANCH_NAME to the URL to go to the branch you want to access.

Branches

The branches are structured to correspond to the videos in the course. The naming convention is CHAPTER#_MOVIE#. As an example, the branch named 02_03 corresponds to the second chapter and the third video in that chapter. Some branches will have a beginning and an end state. These are marked with the letters b for "beginning" and e for "end". The b branch contains the code as it is at the beginning of the movie. The e branch contains the code as it is at the end of the movie. The main branch holds the final state of the code when in the course.

When switching from one exercise files branch to the next after making changes to the files, you may get a message like this:

error: Your local changes to the following files would be overwritten by checkout:        [files]
Please commit your changes or stash them before you switch branches.
Aborting

To resolve this issue:

Add changes to git using this command: git add .
Commit changes using this command: git commit -m "some message"

Instructor

Nayan Saxena

Deep Learning Expert

Check out my other courses on LinkedIn Learning.

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This is a repository for the LinkedIn Learning course Building Secure and Trustworthy LLMs Using NVIDIA Guardrails

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