Improve Amazon Nova migration performance with data-aware prompt optimization
Machine Learning Blog
This article discusses a comprehensive approach for migrating generative AI workloads to Amazon Nova models, focusing on prompt optimization and performance improvement.
- Introduces a four-step workflow for model migration:
- Evaluate source model performance metrics
- Update prompts for Amazon Nova models
- Iteratively optimize prompts
- Conduct A/B testing and deployment
- Uses two prompt optimization techniques:
- Amazon Bedrock prompt optimization
- Data-aware optimization using DSPy framework
- Demonstrated performance improvements across three tasks:
- Text summarization accuracy increased from 77.75% to 87.75%
- Text classification accuracy improved to 87.5%
- Question-answering semantic similarity rose from 52.71 to 57.15
The solution provides a systematic method for migrating and optimizing generative AI workloads to Amazon Nova models, with code and examples available on GitHub.
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