Empower your generative AI application with a comprehensive custom observability solution

This article discusses a comprehensive custom observability solution for generative AI applications built with Amazon Bedrock. It highlights the importance of observability and evaluation for monitoring, troubleshooting, and improving these applications.

Specifically, the article covers:

• Key features of the observability solution, including decorator-based logging, selective logging, data partitioning, human-in-the-loop evaluation, and comprehensive evaluation metrics
• Solution overview, architecture, and implementation details using AWS services like Amazon Data Firehose, AWS Lambda, Amazon S3, AWS Glue, and Amazon Athena
• Step-by-step instructions to get started with the solution using example notebooks from a GitHub repository
• Best practices for deploying and customizing the solution, such as planning call types, using feedback variables, and extending for general workflows
• Conclusion and next steps, encouraging readers to explore and integrate the solution into their generative AI workflows