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

Overview

Discover strategies for mitigating LLM risks through guardrails, including pre-processing techniques against prompt manipulation, output evaluation methods, and open-source frameworks demonstrated in real-world applications.

Syllabus

• Introduction to Large Language Models (LLMs)
Overview of LLM capabilities and applications
Common risks and challenges associated with LLMs
• Understanding Hallucinations in LLMs
Definition and examples of hallucinations
Circumstances leading to model hallucinations
• Prompt Manipulation and Its Implications
How prompt inputs affect LLM outputs
Tactics used for prompt manipulation
• Mitigating Risks in LLM-Powered Applications
Importance of implementing guardrails
Key strategies for risk mitigation
• Pre-Processing Techniques
Input sanitization and validation
Techniques to prevent and detect prompt manipulation
• Output Evaluation and Validation
Methods to evaluate LLM outputs
Strategies for ensuring output reliability and relevance
• Design and Implementation of Guardrails
Algorithmic guardrails to ensure safety and compliance
Usage policies and human oversight
• Open-Source Frameworks for Risk Mitigation
Overview of available tools and frameworks
Demonstration of integrating frameworks into applications
• Case Studies and Real-World Applications
Successful examples of LLM guardrails in action
Lessons learned from real-world deployments
• Future Trends and Developments
Innovative approaches in LLM risk management
Emerging technologies and their potential impact on LLM safety
• Conclusion
Recap of key strategies for safeguarding LLM applications
Recommendations for ongoing risk assessment and management
• Course Review and Q&A Session

Subjects

Computer Science