Shreya Rajpal on Guardrails for Large Language Models
Jan 8, 2024
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Shreya Rajpal, CEO and Cofounder of Guardrails AI, talks about building guardrails for large language model applications, ensuring reliability and safety, and the importance of verifying JSON outputs and string responses. They discuss where the guardrails are built in the model, limitations of prompts, and the impact of guardrails on applications such as chatbots and structured data extraction.
Guardrails AI addresses the reliability and safety issues in large language model applications, ensuring inputs and outputs adhere to specific correctness criteria.
Guardrails AI acts as a sidecar that checks inputs before they are sent to the language model and verifies outputs before they are delivered to the application, providing customizable validators to enforce different correctness criteria.
Deep dives
Guardrails AI: Ensuring Reliability and Safety for Large Language Models
Guardrails AI is an open-source framework that addresses the problem of reliability and safety in large language model applications. While generative AI models are flexible and functional, they often lack reliability. Guardrails AI acts as a firewall around language model APIs, ensuring that inputs and outputs adhere to specific correctness criteria. This includes checking for issues like hallucinations and profanity and enforcing specific functional requirements. Guardrails AI acts as a shell surrounding the language model, safeguarding against dangerous or unreliable outputs.
Implementation of Guardrails AI and its Benefits
Guardrails AI is implemented as a sidecar that runs alongside the language model. It checks inputs before they are sent to the model and verifies outputs before they are delivered to the application. The framework provides a variety of validators that can be customized to enforce different correctness criteria. These validators range from rules-based systems to more complex machine learning techniques. The goal is to ensure that the output aligns with specific requirements, such as proper JSON formatting, language tone, or adherence to regulations. Guardrails AI has been successfully used in various applications, including chatbots, structured data extraction, and contract analysis, providing significant impact and ensuring correctness.
Future of Guardrails AI and User Requests
The future of Guardrails AI involves integrating with more models and providing better visibility and logging capabilities. Users have expressed interest in supporting additional models beyond OpenAI and enhancing the configuration options for guardrails. The focus is on making it easier for users to implement and customize guardrails based on their specific needs. The framework also incorporates a re-asking paradigm to enable models to self-heal by sending incorrect outputs back for correction. Overall, Guardrails AI continues to evolve to address the challenges of reliability and safety in large language models while providing flexibility and ease of use.
Live from the venue of the QCon San Francisco Conference, we are talking with Shreya Rajpal, CEO and Cofounder of Guardrails AI. In this podcast, Shreya shares her insights on building guardrails for large language model (LLM) applications. Rajpal discusses how Guardrails AI assesses the reliability and safety of LLM applications, ensuring any input sent to the model is functionally correct and providing a framework for developers to create their own custom validators.
Read a transcript of this interview: https://bit.ly/47tQUBc
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