aws-samples/amazon-bedrock-samples
Nov 2024 – Nov 2024
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
AWS BedrockAgent DevelopmentAgentic SystemsCloud Services (AWS Bedrock)Code CleanupData Analysis
Aris Tsakpinis
PROFILE
Aris Tsakpinis
Worked on the aws-samples/amazon-bedrock-samples repository to enhance agent evaluation workflows within the RAGAS framework. Developed a new evaluation notebook and supporting utilities using Python and Jupyter Notebooks, focusing on cleaner, shareable outputs and integrated feedback mechanisms. Upgraded the underlying LLM to Claude 3 Haiku, resulting in improved cost efficiency and reduced latency for evaluation tasks. Maintained clear commit discipline and updated lab materials to ensure traceability and reproducibility. The work accelerated agent evaluation processes, provided clearer artifacts for stakeholders, and improved overall workflow reliability, demonstrating strong skills in LLM integration, code hygiene, and cloud-based agentic systems.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
1,939
Activity Months1
Your Network
71 peopleShared Repositories
71
Work History
November 2024
4 Commits • 1 Features
Nov 1, 2024November 2024 (aws-samples/amazon-bedrock-samples): Delivered Agent Evaluation Notebook Enhancements within the RAGAS workflow, including a new evaluation notebook, utilities, cleaner shareable outputs, and integrated feedback; upgraded the LLM to Claude 3 Haiku to reduce cost and improve latency. Major bugs fixed: none reported; minor notebook output and messaging issues resolved to improve reliability. Overall impact: accelerated, repeatable agent evaluation, clearer artifacts for stakeholders, and measurable cost/speed improvements. Technologies/skills demonstrated: Python, Jupyter notebooks, RAGAS workflow, LLM integration (Claude 3 Haiku), notebook hygiene, and strong commit discipline.
4 Commits • 1 Features
Nov 1, 2024November 2024 (aws-samples/amazon-bedrock-samples): Delivered Agent Evaluation Notebook Enhancements within the RAGAS workflow, including a new evaluation notebook, utilities, cleaner shareable outputs, and integrated feedback; upgraded the LLM to Claude 3 Haiku to reduce cost and improve latency. Major bugs fixed: none reported; minor notebook output and messaging issues resolved to improve reliability. Overall impact: accelerated, repeatable agent evaluation, clearer artifacts for stakeholders, and measurable cost/speed improvements. Technologies/skills demonstrated: Python, Jupyter notebooks, RAGAS workflow, LLM integration (Claude 3 Haiku), notebook hygiene, and strong commit discipline.
November 2024
Activity
Loading activity data...
Quality Metrics
Correctness87.6%
Maintainability87.6%
Architecture87.6%
Performance80.0%
AI Usage52.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
AWS BedrockAgent DevelopmentAgentic SystemsCloud Services (AWS Bedrock)Code CleanupData AnalysisJupyter NotebooksLLM EvaluationLLM IntegrationLangChainLangGraphPython DevelopmentPython ProgrammingRAGAS
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline