AI Guardrails

Navigate the intersection of innovation and ethics in the dynamic field of Artificial Intelligence with our intensive course, "AI Guardrails: Secure GenAI Applications" This course is meticulously crafted to provide learners with a condensed, yet profound understanding of the ethical frameworks necessary to guide AI technologies safely.

This course will explore different ways to achieve Guardrails against malicious human interaction with LLM. In the course we will explore various techniques - platforms(AWS Bedrock), models(prompt injection, topical moderation, hallucination) and frameworks (GuardrailsAI, Nemo, Haystack) to achieve GenAI Guardrails. We are still working on Cyber Guardrails and it is not included. The course will not cover DS concepts like fine tuning models to achieve AI Guardrails.

What You'll Learn:

Foundations of AI Ethics: An overview of the ethical considerations critical to AI development, including fairness, privacy, and accountability.
Security: Learn to apply security using model based approach for human access to LLM
Identifying and Implementing AI Guardrails: Learn through concise lectures and interactive scenarios how to establish and enforce guardrails that prevent AI misuse and ensure its alignment with human values
Real-World Applications: Examine case studies that underscore the consequences of neglecting AI guardrails and the steps taken to mitigate such risks
Practical Tools: Gain insights into the tools, frameworks and methodologies for assessing AI systems, identifying potential risks, and ensuring that AI operates within ethical boundaries
User Input Guardrail: Use Open Source Models from Llama 3.1 family (like Prompt-Guard and Llama Guard 3) to detect Prompt Injection and Content moderation
LLM Response Guardrails: Use Open Source fine tuned models like phi3-hallucination-judge and hallucination-evaluation-model focused on Hallucination detection and Answer Relevancy
Prompt based Guardrail: Techniques like LLM-As-A-Judge, Context Relevancy
Guardrails on AWS Bedrock Platform: Learn how to configure, deploy and run Guardrails using AWS Bedrock
Haystack Framework: Introduction to Haystack pipeline
Evaluators: Learn to Evaluate RAG pipelines using metric driven evaluation

Course Highlights:

Focused Curriculum: Dive into the essentials of AI ethics and guardrails, tailored for immediate application.
Hands-On Learning: Participate in engaging exercises that simulate real-world challenges, designed to fit within the course's compact format.
Expert Guidance: Benefit from the distilled wisdom of industry leaders and ethicists, sharing actionable strategies for ethical AI governance.

Who Should Enroll:

This course is ideal for AI developers, data scientists, business leaders, and enthusiasts eager to enhance their understanding of ethical AI practices quickly. Whether you aim to apply ethical considerations to current projects or seek to broaden your knowledge of AI safety measures, this course will equip you with the insights needed for responsible AI development.

Join Us:

Embrace the opportunity to shape the future of AI by embedding ethical considerations and safety measures into the fabric of AI technologies. Enroll in "AI Guardrails: Ensuring Ethical and Safe AI Deployments" and take a significant step towards responsible and safe AI deployment.

What's inside

Learning objectives

Understand the fundamentals of ai guardrails and their importance in ethical ai development.
Retrieval augmented generation: learn about rag, vector store
User input guardrails : learn about prompt injections, user input moderations (hate, violence etc) and ways to detect user input violations
Hallucination: learn about hallucination and detecting hallucination using open source model from hugging face
Evaluators - faithfulness evaluator(llm-as-a-judge), sas evaluator, context relevance evaluator and ragas evalauator

Haystack framework: introduction to haystack pipeline
Guardrails on aws bedrock : learn to configure, deploy and run guardrails on aws bedrock
Explore real world guardrails models using huggingface and colab notebooks
Learn architecture and gain insight on open source frameworks like guardrailsai and nemoguardrails with real-world ai projects.
Learn to implement ai guardrails and nemo framework in ai projects to prevent bias, ensure privacy, and enhance security.

Understand the fundamentals of ai guardrails and their importance in ethical ai development.
Retrieval augmented generation: learn about rag, vector store
User input guardrails : learn about prompt injections, user input moderations (hate, violence etc) and ways to detect user input violations
Hallucination: learn about hallucination and detecting hallucination using open source model from hugging face
Evaluators - faithfulness evaluator(llm-as-a-judge), sas evaluator, context relevance evaluator and ragas evalauator
Haystack framework: introduction to haystack pipeline
Guardrails on aws bedrock : learn to configure, deploy and run guardrails on aws bedrock
Explore real world guardrails models using huggingface and colab notebooks
Learn architecture and gain insight on open source frameworks like guardrailsai and nemoguardrails with real-world ai projects.
Learn to implement ai guardrails and nemo framework in ai projects to prevent bias, ensure privacy, and enhance security.

Syllabus

Introduction

This section covers 10,000 foot view of AI Application and how Guardrails are applied on GenAI Applications. It also highlights what you will learn with the course offerings.

10,000 foot view of different models in the current industry. First, we will learn about different model categories and how models have evolved over time eg- BERT, Language Model, LLM. We will also cover different terminology used in the industry for model development eg- Fine tuning, SFT(Supervise Fine Tuning, RLHF(Reinforcement Learning From Human Feedback)

We will take a deep dive on different inference parameters that will help regulate and manage response generation. These parameters are temperature, top_k, top_p, response length, stop sequences and penalties

In this video, we will use Llama Guard 2 model from Meta to moderate contents from malicious users.

In this video, we will use Multimodal Llama Guard 3-Vision model from Meta to moderate contents with Images from malicious users.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides hands-on experience with open-source models from the Llama 3 family, which are widely used for prompt injection and content moderation

Explores real-world guardrail models using Hugging Face and Colab notebooks, which are essential tools for AI practitioners

Covers AWS Bedrock, which is a platform used by organizations to build and scale generative AI applications

Examines open-source frameworks like GuardrailsAI and NemoGuardrails, which are valuable for implementing AI guardrails in real-world AI projects

Focuses on detecting hallucinations using open-source models from Hugging Face, which is a critical aspect of ensuring the reliability of AI systems

Requires familiarity with Retrieval Augmented Generation (RAG) and vector stores, which may necessitate additional learning for some students

Reviews summary

Practical ai guardrails with frameworks

According to learners, this course offers a practical and hands-on approach to implementing AI guardrails for generative AI applications. Many find the coverage of specific frameworks like GuardrailsAI and Nemo Guardrails particularly valuable, along with the detailed examples using models such as Llama Guard. The integration of guardrails on AWS Bedrock is highlighted as a useful real-world application. While the course is designed for a compact format, providing a focused curriculum, some students suggest that more depth on certain complex topics or specific framework aspects could enhance the learning experience. Overall, it is seen as providing relevant tools and techniques for ensuring ethical and safe AI deployments.

Some areas could benefit from more depth.

"The course is very concise, which is good for getting a quick overview, but sometimes I felt some topics could use deeper dives."

"While it covers a lot of ground, a bit more detail on the advanced features of frameworks like Nemo might be useful."

"The pace was good for a focused introduction, but it assumes some prior technical understanding."

"I appreciated the compact format, but occasionally wished for more elaborate explanations on the underlying mechanisms."

Includes guardrails on a cloud platform.

"The module on implementing Guardrails on AWS Bedrock was a useful addition, showing deployment possibilities."

"Seeing how guardrails fit into a major cloud platform like Bedrock added a layer of practicality."

"Covers configuring and running guardrails within the AWS ecosystem."

Techniques are immediately applicable.

"I found the techniques taught for detecting prompt injection and hallucination using specific models directly applicable."

"This course provides tools and strategies that I could start using in my projects right away."

"It successfully connects the ethical concepts to concrete, implementable technical solutions."

Covers important industry frameworks.

"The sections on GuardrailsAI and Nemo Guardrails are a major strength; these are highly relevant in the field right now."

"Learning about the Haystack framework and RAG evaluation was exactly what I needed for my work."

"This course introduced me to practical frameworks that I can immediately explore for building safer AI applications."

"Good coverage of GuardrailsAI validators and policies."

Provides practical examples and labs.

"The hands-on labs using Colab notebooks were extremely helpful in understanding how to apply the concepts."

"I really appreciated the practical examples demonstrated, especially for prompt injection and hallucination detection."

"The course delivers on its promise of providing hands-on experience with real tools and models."

"Applying the GuardrailsAI concepts in the provided exercises solidified my understanding."

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 AI Guardrails: Responsible AI [Models, Hands-On, Frameworks] with these activities:

Review Key Concepts in Large Language Models

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Reviewing the fundamentals of LLMs will provide a solid foundation for understanding the constraints and guardrails discussed in the course.

Browse courses on Large Language Models

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Review the architecture of transformer models.
Understand the concepts of attention mechanisms.
Familiarize yourself with common LLM evaluation metrics.

Read 'Ethics of Artificial Intelligence'

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Reading this book will provide a strong foundation in the ethical considerations relevant to AI guardrails, enhancing your understanding of the course material.

View Alter Ego: A Novel on Amazon

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Read the chapters on bias, fairness, and accountability.
Reflect on the ethical implications of different AI applications.
Consider how the ethical principles discussed in the book can be applied to your own work.

Read 'Responsible AI: A Global Policy Framework'

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Reading this book will provide a broader understanding of the ethical and policy considerations surrounding AI, complementing the technical aspects covered in the course.

View Alter Ego: A Novel on Amazon

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Read the introduction and conclusion to understand the book's scope.
Focus on chapters discussing fairness, accountability, and transparency in AI.
Take notes on key policy recommendations and their implications.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Experiment with Different LLM Inference Parameters

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Experimenting with inference parameters like temperature and top_p will help you understand their impact on LLM output and how to use them to control AI behavior.

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Choose an LLM API or local model to experiment with.
Vary the temperature and top_p parameters and observe the changes in the generated text.
Document the effects of different parameter settings on the output quality and diversity.

Implement a Simple Prompt Injection Detector

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Implementing a basic prompt injection detector will provide hands-on experience with identifying and mitigating a common AI security vulnerability.

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Collect a dataset of prompt injection examples.
Develop a rule-based or machine learning model to detect injections.
Test the detector against new, unseen prompts.
Evaluate the detector's performance using appropriate metrics.

Create a Presentation on AI Hallucination Mitigation Techniques

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Creating a presentation on hallucination mitigation techniques will solidify your understanding of this critical AI challenge and potential solutions.

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Research different techniques for detecting and mitigating AI hallucinations.
Organize the information into a clear and concise presentation.
Include examples and case studies to illustrate the techniques.
Practice the presentation to ensure a smooth delivery.

Contribute to an Open Source AI Guardrails Project

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Contributing to an open-source AI guardrails project will provide valuable hands-on experience with real-world AI safety challenges and collaborative development.

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Identify an open-source AI guardrails project on platforms like GitHub.
Review the project's documentation and contribution guidelines.
Identify a bug or feature to work on.
Submit a pull request with your changes.

Career center

Learners who complete AI Guardrails: Responsible AI [Models, Hands-On, Frameworks] will develop knowledge and skills that may be useful to these careers:

AI Governance Specialist

The role of an AI Governance Specialist is crucial in today's technology landscape. These professionals help organizations navigate the complexities of AI ethics, compliance, and risk management. This course, with its foundations of AI ethics, identification of AI Guardrails, and real-world applications, builds a strong foundation for this path. The course's focus on tools, frameworks, and practical methodologies for assessing AI systems makes it particularly relevant. The knowledge gained from prompt injection detection, content moderation, and the configuration of Guardrails using AWS Bedrock are crucial for anyone aiming to work as an AI Governance Specialist.

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AI Risk Analyst

An AI Risk Analyst identifies, assesses, and mitigates risks associated with AI systems. This role involves developing risk management frameworks, conducting risk assessments, and implementing controls to minimize potential harm. This course, with its foundations of AI ethics, identification of AI Guardrails, and real-world applications, builds a strong foundation for this path. The course's focus on tools, frameworks, and methodologies for assessing AI systems will be particularly relevant.

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AI Security Engineer

An AI Security Engineer focuses on protecting AI systems from various threats, including adversarial attacks, data breaches, and model vulnerabilities. This course's emphasis on security, identifying and implementing AI Guardrails, and mitigating risks is directly applicable to this role. The hands-on learning, specifically around user input guardrails, LLM response guardrails, and prompt-based guardrails, would be beneficial. Anyone seeking to become an AI Security Engineer would find the material on prompt injection and content moderation particularly useful.

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Machine Learning Ethicist

A Machine Learning Ethicist ensures that AI systems are developed and deployed in a responsible and ethical manner. This role involves assessing potential biases, ensuring fairness, and promoting transparency in AI algorithms. This course, with its foundations of AI ethics, real-world applications, and practical tools, builds a strong foundation for this path. The assessment of AI systems and the emphasis on ensuring AI operates within ethical boundaries are particularly relevant for this role. This course also includes the study of open source frameworks like GuardrailsAI and NemoGuardrails.

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AI Auditor

An AI Auditor evaluates the ethical and responsible use of AI systems within an organization. This role involves assessing AI algorithms for bias, ensuring compliance with regulations, and providing recommendations for improvement. This course, with its foundations of AI ethics, real-world applications, and practical tools, builds a strong foundation for this path. The emphasis on fairness, privacy, and accountability, as well as insights into tools and methodologies for assessing AI systems, will be particularly relevant.

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AI Compliance Officer

An AI Compliance Officer is responsible for ensuring that an organization's AI practices adhere to relevant laws, regulations, and industry standards. This individual develops and implements policies, monitors AI systems for compliance, and addresses any potential violations. This course, with its foundations of AI ethics, identification of AI Guardrails, and real-world applications, builds a strong foundation for this path. The coverage of the AWS Bedrock platform will be particularly useful. This course also covers prompt injection detection and content moderation.

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AI Educator

An AI Educator instructs students, professionals, or the general public on the principles, applications, and ethical considerations of artificial intelligence. This role involves developing curricula, delivering lectures, and facilitating discussions on AI-related topics. This course, with its foundations of AI ethics, identification of AI Guardrails, and real-world applications, may be useful. An emerging area of education is teaching people how to detect prompt injection in LLMs. The course includes information about using Open Source Models from Llama 3.1 family (like Prompt-Guard and Llama Guard 3) to detect Prompt Injection and Content moderation.

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Research Scientist

A Research Scientist investigates fundamental questions and develops new theories or technologies in the field of artificial intelligence. This role typically requires an advanced degree (master's or phd). This course's content on AI Guardrails, responsible AI models, and frameworks may be helpful. The understanding developed in this course could inform research projects focused on enhancing the safety, security, and alignment of AI systems. The syllabus for this course includes discussion of the GuardrailsAI Framework and the Nemo Guardrails Framework.

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AI Policy Advisor

AI Policy Advisors work with governments or organizations to develop policies and guidelines for the responsible development and deployment of AI. This role involves researching AI trends, analyzing potential impacts, and formulating policy recommendations. This course, with its foundations of AI ethics, identification of AI Guardrails, and real-world applications, may prove useful. The course's examination of the consequences of neglecting AI guardrails can inform policy discussions and guide the development of effective regulations.

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Data Privacy Consultant

Data Privacy Consultants advise organizations on how to protect personal data and comply with privacy regulations such as GDPR and CCPA. This role involves assessing data privacy risks, developing privacy policies, and implementing data security measures. This course, with its foundations of AI ethics and examination of case studies where AI guardrails were neglected, may be useful for this career path. This course emphasizes fairness, privacy, and accountability, which are all important in the field of data privacy.

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AI Product Manager

An AI Product Manager oversees the development and launch of AI-powered products. This role involves defining product strategy, gathering requirements, and working with engineering teams to bring AI solutions to market. This course, with its foundations of AI ethics, real-world applications, and tools for assessing AI systems, may provide some value for this role. The knowledge of AI guardrails and their applications can inform product development decisions and ensure that AI products are developed responsibly.

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Computational Social Scientist

Computational Social Scientists use computational methods to study social phenomena. They may use AI techniques to analyze large datasets, model social behaviors, and understand societal impacts. This course, with its foundations of AI ethics, identification of AI Guardrails, and real-world applications, may be useful.

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Data Scientist

Data Scientists analyze large datasets to extract insights and develop data-driven solutions. While the course has a strong focus on ethics and guardrails, the knowledge of AI constraints and model evaluation techniques may be useful. For example, this course covers topics such as prompt injection, content moderation, and the configuration of Guardrails using AWS Bedrock.

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Technical Writer

Technical Writers create documentation for software, hardware, and other technical products. The course's comprehensive coverage of AI Guardrails, frameworks, and models may be useful for someone in this field. The knowledge gained about tools, frameworks, methodologies for assessing AI systems and guardrails using the AWS Bedrock Platform, can be used in documentation.

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Software Engineer

Software Engineers design, develop, and maintain software systems. While this course's primary focus is not on software engineering, the understanding of AI Guardrails, NemoGuardrails, and Haystack Framework could be of some use. The course's overview of ethical considerations and security using model based approach, could provide some value.

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