AI System Evaluation and Guardrails Guide
This comprehensive guide delves into ensuring the reliability and safety of AI systems in production. Explore essential techniques like prompt testing, hallucination detection, and robust output validation to build trustworthy AI. Discover strategies for designing effective safety filters and guardrails, complete with real-world tools and implementation advice.
Chapters
- 01 AI Red Teaming: Proactively Expose and Secure System Weaknesses 16m
- 02 Introduction to AI Guardrails: Principles & Architecture 13m
- 03 Output Validation & Quality Assurance for Diverse AI Systems 18m
- 04 Regression Testing for AI: Preventing Unintended Consequences 16m
- 05 The Imperative of AI Reliability: Evaluation & Guardrails 12m
- 06 Continuous Monitoring & MLOps for AI Reliability in Production 16m
- 07 Setting Up Your AI Reliability Toolkit: Environment & Essentials 9m
- 08 Foundations of AI System Evaluation: Metrics & Benchmarking 16m
- 09 Designing & Building Comprehensive Guardrail Systems 18m
- 10 Detecting & Mitigating Hallucinations in Generative AI 18m
- 11 Implementing Input & Output Guardrails: Safety & Compliance Filters 280m
- 12 Systematic Prompt Testing for LLM Performance and Safety 15m