19 — Troubleshoot Generative AI Applications

19 — Troubleshoot Generative AI Applications

Module Introduction

• Introduction
• Generative AI troubleshooting overview
• Building troubleshooting frameworks
• Common generative AI issues and challenges
• Module roadmap

Error Monitoring

• Introduction
• Common failure modes in generative AI systems
• AWS services for error monitoring and observability
• Implementing observability with AWS X-Ray
• CloudWatch monitoring for integration points
• Error handling and retry strategies

Developing Troubleshooting Frameworks

• Introduction
• Core troubleshooting framework components
• Golden datasets for hallucination detection
• Output diffing techniques for response consistency
• Reasoning path tracing for logical error identification

Increasing Troubleshooting Efficiency

• Introduction
• Benefits of efficient troubleshooting approaches
• Log analysis with CloudWatch Logs Insights
• Performance profiling with AWS X-Ray
• Amazon Q Developer for error pattern recognition

Resolving Content Handling Issues

• Introduction
• Common content handling issues in foundation models
• Context window overflow diagnostics
• Dynamic chunking strategies
• Prompt design optimization for content handling
• Truncation-related error analysis

Resolving Foundation Model Integration Issues

• Introduction
• Foundation model integration overview
• Comprehensive error logging for foundation model integrations
• Request validation for API integrity
• API response analysis and error resolution

Resolving Prompt Engineering Issues

• Introduction
• Prompt engineering problem identification
• Prompt testing frameworks implementation
• Version comparison and optimization strategies

Resolving Retrieval System Issues

• Introduction
• Retrieval system architecture and components
• Model response relevance analysis
• Drift monitoring and detection
• Chunking and preprocessing remediation
• Vector search performance optimization

Module Summary

• Recap and next steps
• Resources