IBM/api-integrated-llm-experiment
Mar 2025 – Mar 2025
1 Month active
Languages Used
JSONPythonShell
Technical Skills
AI IntegrationAI Model ConfigurationAPI IntegrationBackend DevelopmentCode RefactoringConfiguration Management
Anupama Murthi
PROFILE
Anupama Murthi
Developed and enhanced the LLM experimentation workflow within the IBM/api-integrated-llm-experiment repository, focusing on prompt refinement, robust parsing, and workflow automation. Leveraged Python and JSON to update prompt configurations, sharpen model responses, and improve the reliability of evaluation metrics. Introduced new parsing modules and automated scripts for data preparation, scoring, and aggregation, streamlining the experimental process for LLM-based agent research. Emphasized backend development, code refactoring, and configuration management to ensure stability and scalability. The work enabled faster experimentation cycles, clearer evaluation metrics, and repeatable, data-driven decision-making, establishing a solid foundation for ongoing and future LLM experiments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
3
Lines of code
2,699
Activity Months1
Your Network
997 peopleSame Organization
@ibm.com988
Arun VMember
Aayush-PanchalMember
Aayush KumarMember
Abhijit DeokarMember
Abhina SreeMember
Abhishek KumarMember
Abhishek Kr SrivastavMember
Abraham Lara GranadosMember
Adapa ChathuryaMember
Work History
March 2025
11 Commits • 3 Features
Mar 1, 2025March 2025 remained focused on delivering a robust LLM experimentation workflow within IBM/api-integrated-llm-experiment, aligning prompt management, parsing/scoring reliability, and automation to accelerate evaluation cycles and business decisions. The team shipped three primary feature areas, targeted fixes to improve stability, and established a scalable foundation for future experiments.
11 Commits • 3 Features
Mar 1, 2025March 2025 remained focused on delivering a robust LLM experimentation workflow within IBM/api-integrated-llm-experiment, aligning prompt management, parsing/scoring reliability, and automation to accelerate evaluation cycles and business decisions. The team shipped three primary feature areas, targeted fixes to improve stability, and established a scalable foundation for future experiments.
March 2025
Activity
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Quality Metrics
Correctness76.4%
Maintainability78.2%
Architecture69.2%
Performance63.6%
AI Usage47.4%
Skills & Technologies
Programming Languages
JSONPythonShell
Technical Skills
AI IntegrationAI Model ConfigurationAPI IntegrationBackend DevelopmentCode RefactoringConfiguration ManagementData CleaningData ParsingData ProcessingExperiment ManagementExperimentationLLMLLM DevelopmentLLM IntegrationLLM Operations
Repositories Contributed To
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