30 Days of LLMs: Day 25 - Controlling LLM Outputs with Shreya Rajpal
For Day 25 of the W&B 30 Days of LLMs, Shreya Rajpal, co-founder and CEO of Guardrails AI, explores how to control LLM outputs using the Guardrails AI framework.
Created on December 18|Last edited on December 22
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30 Days of LLMs Contest
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Day 25 - Controlling LLM Outputs with Shreya Rajpal
Welcome to Day 25 of our complimentary course, "Building LLM-Powered Apps". In this installment, Shreya Rajpal, co-founder and CEO of Guardrails AI, shares insights on effectively managing Large Language Model (LLM) outputs with the innovative approach of the Guardrails AI framework.
Chapter Highlights
- Tackling LLM Output Issues: Explore the challenges like fragility and unpredictability in LLM outputs and the importance of their regulation for effective use.
- Limitations of Prompts and Model Revisions: Discover why solely depending on prompts or model refinements may not guarantee consistent LLM outputs.
- Introduction to Guardrails AI: Examine how the Guardrails AI framework assists in better managing LLM outputs, ensuring their dependability and suitability.
- Developing a Verification Component: Grasp the design of incorporating a verification component with LLMs for conducting checks specific to applications.
- Real-world Implementation Cases: Learn about real-life applications of verification mechanisms, such as screening for private information and confirming content suitability.
Key Course Information
- No deep machine learning knowledge is needed, just some familiarity with Python programming.
- Strategies for continual enhancement of your LLM applications.
- Unique perspectives on the LLM tools used by Weights & Biases.
Free Enrollment
Preview of the Next Chapter
Stay tuned for our forthcoming chapter, where we'll delve into safety aspects and prompt integration in LLM applications.
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