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
Developed a feedback-based prompt optimization pipeline for Amazon Bedrock within the aws-samples/amazon-nova-samples repository, focusing on automating prompt evaluation and iterative refinement. Leveraging Python and Jupyter Notebook, the solution introduced separate optimization and rewriting models to systematically analyze errors and enhance prompt quality. This approach enabled a substantial increase in classification accuracy, raising it from 52% to 92%, and reduced the need for manual prompt tuning. The work established a scalable workflow for prompt engineering in Bedrock deployments, demonstrating depth in data analysis, machine learning, and LLM optimization while laying a foundation for future automated prompt improvement initiatives.
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
1690 peopleShared Repositories
79
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
Activity
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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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