Hallucinations, Prompt Manipulations, and Mitigating Risk: Putting Guardrails around your LLM-Powered Applications

Overview

Explore innovative techniques to protect your LLM-powered applications from risks like hallucinations and prompt manipulation. This session offers insights into effective pre-processing techniques, methods for evaluating outputs, and demonstrates how open-source frameworks can be utilized in practical scenarios.

Ideal for those interested in artificial intelligence and computer science, this event is hosted by YouTube, making cutting-edge information readily accessible to learners.

Syllabus

• Introduction to LLM Risks
Overview of hallucinations and prompt manipulations
Importance of guardrails in LLM applications
• Understanding Prompt Manipulations
Types of prompt manipulation techniques
Impact on output quality and reliability
• Pre-processing Techniques
Input validation and sanitization
Contextual awareness and prompt structuring
• Output Evaluation Methods
Automated evaluation metrics
Human-in-the-loop feedback systems
• Implementing Guardrails
Role of safety layers and filters
Balancing creativity with control
• Open-Source Frameworks for LLM Guardrails
Overview of available tools and libraries
Integration with real-world applications
• Case Studies and Real-World Applications
Successful implementation examples
Lessons learned and best practices
• Mitigating Risk in Dynamic Environments
Continuous monitoring and updating guardrails
Adaptive strategies for evolving threats
• Closing Remarks
Summary of strategies and tools
Future directions and emerging technologies in LLM safety

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Subjects

Computer Science