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Build Guardrails for Your AI with Open Source

IntermediateGuided Project

Protect your company and brand reputation, enhance user safety and trust with advanced measures, ensuring ethical and responsible interactions. Learn to build guardrails quickly and inexpensively using open source technologies.

4.5 (95 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 330

Skills You Will Learn

  • Generative AI, LLM, Open Source

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 2 hours

Platform

  • SkillsNetwork

Last Update

  • May 11, 2025
About this Guided Project

A Look at the Project Ahead

"It takes 20 years to build a reputation and five minutes to ruin it. If you think about that, you'll do things differently." - Warren Buffett

Artificial Intelligence holds tremendous promise for almost any business or a brand. At the same time it can also be a existential threat to the reputation of any organization that throws caution to the wind and unleashes unrestrained AI. The whole purpose of Generative AI is to generate a conversation between your company, your organization, your brand and your customers, partners, users etc. This conversation must be conducted in a respectful, inclusive, and polite way and it must be on topic that is acceptable to both you and your audience. "Guardrails" is the term used to describe technology to keep AI conversations on topic and with the proper tone to protect your organization's reputation. In our opinion, no AI should be deployed in production without proper guardrails in place.
     
In this project you will learn what "guardrails" are in the context in Artificial Intelligence (AI), and how you can implement them on your own AI using open-source tools. By the end of this guided project, you will know:
  • Why guardrails are important for AI.
  • How to implement guardrails on your own AI.
  • The current limitations of open-source guardrails and how to overcome them.

What You'll Need

This guided project requires basic knowledge of Python, LangChain, and Git. In the third section we will delve into details of guardrails implementations, and familiarity with open-source development and debugging could come in handy.

However, don't worry if you don't have much experience with the above. This project will explain each step along the way, and you may learn as-you-need along the way.

IBM Skills Network Labs environment also comes with many technical requisites pre-installed to save the hassle of setting everything up, so we won't be dealing with any environment issues.

Instructors

Efkan Serhat Goktepe

Developer | Architect

Efkan is a 4th year student in Computer Science at University of Toronto. Efkan is currently working at IBM as a Software Architect. Contact: efkan@ibm.com.

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Contributors

Leon Katsnelson

Director & CTO, IBM Developer Skills Network

I've had a very productive career in tech. I've touched many areas from mainframe, to manufacturing automation (IoT), to databases, big data, data science and AI, blockchain, and of course full stack and cloud-native development and DevOps. I started my career in test and QA, did quite a bit of development, product management, team leadership, and people management before becoming an executive. I had some great wins including bringing to market a billion $ product. And had some failures along the way. But throughout my career, one thing has always remained constant. I learned everything I could and used every chance I had to get a new skill. My goal in life is to help those who have an appetite for learning to acquire knowledge and skill to build their career or simply become better users of the latest tech.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Sina Nazeri

Data Scientist at IBM

I am grateful to have had the opportunity to work as a Research Associate, Ph.D., and IBM Data Scientist. Through my work, I have gained experience in unraveling complex data structures to extract insights and provide valuable guidance.

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