aws-samples/amazon-nova-samples
Apr 2025 – Apr 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
AWS BedrockData AnalysisLLM OptimizationMachine LearningPrompt EngineeringPython
Gary Lo
PROFILE
Gary Lo
Gary Lo developed a feedback-based prompt optimization pipeline for the aws-samples/amazon-nova-samples repository, targeting automated improvement of prompt accuracy in Amazon Bedrock models. Using Python and Jupyter Notebook, Gary designed a system that evaluates model prompts, analyzes errors, and iteratively refines prompts through separate optimization and rewriting models. This approach automated the feedback loop, reducing the need for manual prompt tuning and increasing classification accuracy from 52% to 92%. The work demonstrated depth in prompt engineering and LLM optimization, establishing a scalable workflow for Bedrock deployments and laying a technical foundation for future enhancements in automated classification tasks.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
5,672
Activity Months1
Your Network
1455 people
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 focused on delivering a high-impact feature in the aws-samples/amazon-nova-samples repo: a Feedback-based Prompt Optimization Pipeline for Amazon Bedrock. This automated pipeline evaluates model prompts, analyzes errors, and refines prompts iteratively using separate optimization and rewriting models. The result is a substantial accuracy uplift in classification tasks—from 52% to 92%—driving more reliable automated classification and reducing manual prompt tuning. The work is underpinned by a single commit that introduces the new use case and lays the foundation for scalable prompt engineering.
1 Commits • 1 Features
Apr 1, 2025April 2025 focused on delivering a high-impact feature in the aws-samples/amazon-nova-samples repo: a Feedback-based Prompt Optimization Pipeline for Amazon Bedrock. This automated pipeline evaluates model prompts, analyzes errors, and refines prompts iteratively using separate optimization and rewriting models. The result is a substantial accuracy uplift in classification tasks—from 52% to 92%—driving more reliable automated classification and reducing manual prompt tuning. The work is underpinned by a single commit that introduces the new use case and lays the foundation for scalable prompt engineering.
April 2025
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage100.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
AWS BedrockData AnalysisLLM OptimizationMachine LearningPrompt EngineeringPython
Repositories Contributed To
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