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New short course! Quality and Safety for LLM Applications, created with WhyLabs (an AI Fund portfolio company) and taught by Bernease Herman, shows how you can mitigate hallucinations, data leakage, and jailbreaks. Come learn more in the course, available now!

105,877 просмотров • 2 лет назад •via X (Twitter)

Комментарии: 8

Фото профиля EricF
EricF2 лет назад

@WhyLabs @bernease Interesting thanks for the guidance

Фото профиля Arsalan Ali
Arsalan Ali2 лет назад

@WhyLabs @bernease Thank you Sir @AndrewYNg

Фото профиля Steven Song
Steven Song2 лет назад

@WhyLabs @bernease Cool! Which level would you say this is for? Ms. Herman

Фото профиля BEDI ACTIVE RIGHT NOW -e/acc FUTURE OF EDUCATION
BEDI ACTIVE RIGHT NOW -e/acc FUTURE OF EDUCATION2 лет назад

@WhyLabs @bernease Appreciate your thoughts, quite valid.

Фото профиля Chukwudi
Chukwudi2 лет назад

@WhyLabs @bernease Snr man 🙌🏽❤️❤️❤️🙌🏽Andrew

Фото профиля Max Chan
Max Chan2 лет назад

@WhyLabs @bernease 👍

Фото профиля Nimit Shah
Nimit Shah2 лет назад

@WhyLabs @bernease Has anyone tried the code in this course? I keep getting helpers attribute error for visualize_langkit_metric or any helpers module.

Фото профиля Vincent Granville
Vincent Granville2 лет назад

@WhyLabs @bernease How to Avoid Sensitive Data Leakage in LLM & RAG Frameworks: Case Study

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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:

Andrew Ng

106,795 просмотров • 1 год назад