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  "full_text": "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,  @ShreyaR.\n\nI 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. \n\nFortunately, 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. \n\nThis 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.\n\nYou'll implement these guardrails in the context of a RAG-powered customer service chatbot for a small pizzeria. Specifically, you'll:\n- Explore common failure modes like hallucinations, going off-topic, revealing sensitive information, or responses that can harm the pizzeria's reputation.\n- Learn to mitigate these failure modes with input and output guards that check inputs and/or outputs\n- Create a guardrail to prevent the chatbot from discussing sensitive topics, such as a confidential project at the pizza shop\n- Detect hallucinations by ensuring responses are grounded in trusted documents\n- Add a Personal Identifiable Information (PII) guardrail to detect and redact sensitive information in user prompts and in LLM outputs\n- Set up a guardrail to limit the chatbot’s responses to topics relevant to the pizza shop, keeping interactions on-topic\n- 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 \n\nGuardrails 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.\n\nPlease sign up here: https://t.co/C1fwsOn9yy",
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