DeepLearning.AI reposted this
Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of Landing AI
New short course: Safe and Reliable AI via Guardrails! Learn to create production-ready, reliable LLM applications with guardrails in this new course, built in collaboration with Guardrails AI and taught by its CEO and co-founder, Shreya Rajpal. I see many companies worry about the reliability of LLM-based systems -- will they hallucinate a catastrophically bad response? -- which slows down investing in building them and transitioning prototypes to deployment. That LLMs generate probabilistic outputs has made them particularly hard to deploy in highly regulated industries or in safety-critical environments. Fortunately, there are good guardrail tools that give a significant new layer of control and reliability/safety. They act as a protective framework that can prevent your application from revealing incorrect, irrelevant, or confidential information, and they are an important part of what it takes to actually get prototypes to deployment. This course will walk you through common failure modes of LLM-powered applications (like hallucinations or revealing personally identifiable information). It will show you how to build guardrails from scratch to mitigate them. You’ll also learn how to access a variety of pre-built guardrails on the GuardrailsAI hub that are ready to integrate into your projects. You'll implement these guardrails in the context of a RAG-powered customer service chatbot for a small pizzeria. Specifically, you'll: - Explore common failure modes like hallucinations, going off-topic, revealing sensitive information, or responses that can harm the pizzeria's reputation. - Learn to mitigate these failure modes with input and output guards that check inputs and/or outputs - Create a guardrail to prevent the chatbot from discussing sensitive topics, such as a confidential project at the pizza shop - Detect hallucinations by ensuring responses are grounded in trusted documents - Add a Personal Identifiable Information (PII) guardrail to detect and redact sensitive information in user prompts and in LLM outputs - Set up a guardrail to limit the chatbot’s responses to topics relevant to the pizza shop, keeping interactions on-topic - Configure a guardrail that prevents your chatbot from mentioning any competitors using a name detection pipeline consisting of conditional logic that routes to an exact match or a threshold check with named entity recognition Guardrails are an important part of the practical building and deployment of LLM-based applications today. This course will show you how to make your applications more reliable and more ready for real-world deployment. Please sign up here: https://lnkd.in/gRU6qhzN
Thanks for sharing. For those looking for data science/engineering/analyst internships or new grad roles, you can apply to hundreds of them at https://tinyurl.com/2dbps6em for free. New positions are added daily.
Very important to understand the common failure of LLM such as: - hallucinations - going off topics - revealing sensitive topics Let's find out if Guardrails also covers the other common failure such as: - infinite loops - insecure response handling - prompt injection
I think it‘s worth to have a closer look Jan-Philipp Schreiter & Horst Hellbrück .
Yay! Guardrails are key to ensuring safe and reliable AI deployment. Excited to learn more about creating production-ready LLM applications. Andrew Ng
An essential component of AI governance is continuous monitoring to detect and intercept harmful AI outputs. It’s equally important to provide corrective feedback, enabling systems to improve and evolve their future outcomes.
Thank you, Andrew Ng and Shreya Rajpal!! This following course very useful for anyone looking to bridge the gap between LLM prototypes and real-world deployment. The focus on guardrails is crucial, especially in ensuring compliance and maintaining user trust. More so for the #Healthcare applications. Looking forward to seeing the innovative solutions that come out of this training and looking forward to learn and implement the learning in the Life Sciences and Healthcare setting! 🚀
Andrew Ng, sounds like a solid setup for tackling those LLM issues. Guardrails could really boost reliability in tricky environments. What do you think?
This course seems vital for teams building LLM applications. Ensures reliability and user trust. Examining common failure modes offers practical insights. A deep dive into guardrail implementation is beneficial. What unique challenges have you encountered in deploying LLMs?
Andrew Ng This course sounds like a must-have for anyone deploying LLM-based apps in regulated or sensitive environments. Guardrails are essential for safe, reliable AI, especially in industries where accuracy and confidentiality are non-negotiable. Excited to see the practical examples, like handling sensitive data and keeping the chatbot’s responses on-topic for the pizza shop. How customizable are the pre-built guardrails on GuardrailsAI? Looking forward to learning more!