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The Cloudcast
Validation and Guardrails for LLMs
Feb 21, 2024
Shreya Rajpal, CEO of Guardrails AI, discusses the importance of validation and guardrails for LLMs in AI models. Topics include the need for guardrails, controlling outputs, validation techniques, and practical examples in customer support. They also explore Guardrails AI's central hub for customizable solutions and ways to connect with the speaker.
27:27
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Quick takeaways
- Guardrails AI offers validation techniques to address factuality and hallucinations in AI applications.
- Guardrails Hub provides reusable validation solutions for implementing guardrails in AI, customizable to specific requirements.
Deep dives
Guardrails AI: Validation and Accuracy of AI
Guardrails AI provides a solution for validating and ensuring accuracy in AI applications, particularly in the context of customer support chatbots. Factuality and hallucinations are key concerns when implementing generative AI, and Guardrails AI offers validation techniques to address these issues. The company's approach involves an input guard, which checks prompts for specific risks such as PII leakage or off-topic conversations, and an output guard, which ensures compliance, factuality, and etiquette in the generated responses. The architecture emphasizes the critical path, providing a safety layer during runtime to validate and verify AI outputs before they are sent to the application.
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Discover
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