Enterprise technology company builds comprehensive LLM evaluation framework
A leading enterprise technology company partnered with Yearling AI to develop a sophisticated solution for evaluating Large Language Models for integration into their data ecosystem, ensuring governance compliance and optimal performance.
The Challenge
The client needed to determine the best LLM for their enterprise environment, but faced several challenges in evaluating the many commercial and open-source options available in today's rapidly evolving AI landscape.
With data spread across multiple systems and diverse business units with unique requirements, the organization required a standardized way to measure LLM accuracy, response time, and reasoning ability while adhering to strict governance controls.
Key Pain Points:
- Data spread across multiple systems made it difficult to assess an LLM's ability to retrieve and integrate information
- Need for AI solution that adheres to strict role-based access controls
- Lacked standardized way to measure LLM accuracy, response time, and reasoning ability
- Diverse business units had unique data access and query requirements
- Needed objective comparison between commercial LLMs and open-source alternatives
The Solution
The comprehensive solution developed by Yearling AI evaluates LLMs on their ability to access enterprise data through APIs, respect role-based access controls, and provide accurate insights across varying levels of complexity.
The framework successfully benchmarked both open-source and commercial LLMs including Claude, OpenAI, Gemini, DeepSeek, Llama 3, Mistral, and others, providing detailed performance metrics that enabled informed deployment decisions.
How It Works:
The Results
The benchmarking framework met the client's LLM evaluation needs and laid the groundwork for ongoing AI governance and optimization. The solution provided objective, data-driven insights for selecting the optimal LLM for their production environment.
Key Outcomes:
Future Roadmap
The framework will be enhanced with vector database integration, multi-modal support, advanced monitoring capabilities, and workflow automation to facilitate adoption of new LLM capabilities while maintaining governance standards.
Project Overview
Technologies Used
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