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.

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Introduction

00:00 • 2min

Exploring the Importance of Validation and Guardrails for Language Models

02:24 • 15min

Importance of Validation Techniques for Models

17:20 • 6min

Exploring Guardrails AI and Open Source Marketplace Hub

22:59 • 3min

Avenues to Connect with the Speaker

25:51 • 2min

Shreya Rajpal (@ShreyaR, CEO @guardrails_ai ) talks about the need to provide guardrails and validation of LLM’s, along with common use cases and Guardrail AI’s new Hub.

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SHOW NOTES:

Topic 1 - Welcome to the show. Before we dive into today’s discussion, tell us a little bit about your background.

Topic 2 - Our topic today is the validation and accuracy of AI with guardrails. Let’s start with the why… Why do we need guardrails for LLMs today?

Topic 3 - Where and how do you control (maybe validate is a better word) outputs from LLM’s today? What are your thoughts on the best way to validate outputs?

Topic 4 - Will this workflow work with both closed-source (ChatGPT) and opensource (Llama2) models? Would this process apply to training/fine-tuning or more for inference? Would this potentially replace humans in the loop that we see today or is this completely different?

Topic 5 - What are some of the most common early use cases and practical examples? PII detection comes to mind, violation of ethics or laws, off-topic/out of scope, or simply just something the model isn’t designed to provide?

Topic 6 - What happens if it fails? Does this create a loop scenario to try again?

Topic 7 - Let’s talk about Guardrails AI specifically. Today you offer an open-source marketplace of Validators in the Guardrails Hub, correct? As we mentioned earlier, almost everyone’s implementation and guardrails they want to implement will be different. Is the best way to think about this as building blocks using validators that are pieced together? Tell everyone a little bit about the offering

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