Project Overview
A comprehensive research initiative to evaluate and benchmark Snwolley AI’s performance against industry-leading large language models (ChatGPT and Claude). This project employed systematic testing methodologies, custom scripting, and advanced prompt engineering techniques to generate quantifiable performance metrics across multiple dimensions.
Team Members
- Prince Djangmah – Research & Development Intern, Software Developer
Technologies & Frameworks
- Custom Python scripts for automated testing
- Prompt engineering frameworks
- Comparative analysis methodologies
- Performance metrics collection systems
- API integration for model testing
Project Status
Completed. Comprehensive benchmarking framework established and extensive testing conducted across all three AI models with documented findings.
Key Research Components
Benchmarking Framework Development:
- Design of standardized testing protocols
- Creation of custom scripts for automated evaluation
- Development of metrics for measuring response quality, accuracy, and performance
- Establishment of comparable testing conditions across platforms
Prompt Engineering:
- Strategic prompt design for consistent model evaluation
- Testing across multiple use cases and complexity levels
- Documentation of prompt variations and their impacts
- Analysis of model-specific optimization techniques
Comparative Analysis:
- Side-by-side evaluation of Snwolley AI, ChatGPT, and Claude
- Assessment across dimensions including accuracy, response time, context handling, and domain-specific knowledge
- Identification of strengths and limitations for each platform
- Documentation of use-case recommendations
Research Insights
This project provided deep insights into the operational characteristics of modern large language models. The benchmarking process revealed nuanced differences in how each AI system handles various types of queries, maintains context over extended conversations, and performs domain-specific tasks.
The development of automated testing scripts proved essential for maintaining consistency across hundreds of evaluation scenarios. Prompt engineering emerged as a critical skill, with significant performance variations observed based on prompt structure and specificity.
Impact
The benchmarking framework and findings provide Npontu Technologies with data-driven insights for AI tool selection and optimization. The research contributes to understanding AI model capabilities and limitations, informing strategic decisions about AI integration in organizational workflows.