An enterprise software organization approached us to design and implement a comprehensive LLM evaluation framework to measure, monitor, and improve the quality and consistency of large language model outputs across production use cases. Our team helped client develop LLM system to test and manage all AI systems easily and effectively.
Industry: Enterprise Software
Location: USA
Requirement: LLM Eval
As the client expanded the use of LLMs across multiple workflows, it became quite difficult to measure and test the quality of results. The client faced the following challenges:
Without a structured evaluation system, quality issues often surfaced only after deployment.
Imperym Labs developed an automated LLM evaluation framework aligned with the client’s production workflows. The solution introduced structured evaluation datasets and automated scoring pipelines to test LLM outputs across clearly defined quality parameters. Evaluations were executed automatically whenever a model, prompt, or configuration was updated and gave reliable & measurable reports. The framework measured performance across the following key metrics:
Evaluation results were captured, scored, and compared across model versions, enabling complete model evaluation before production deployment.
| Layer | Description |
|---|---|
| Model | OpenAI GPT-4 and GPT-3.5 |
| Evaluation Framework | Custom automated evaluation pipelines |
| Language / Runtime | Python 3.11 |
| Evaluation Metrics | Accuracy, relevance, completeness, consistency, hallucination rate |
| Prompt Versioning | Git-based prompt tracking |
| Automation | CI integrated evaluation workflows |
| Reporting | Structured scorecards and comparison reports |
| Deployment | Docker based services |
The LLM evaluation framework delivered measurable improvements in quality control and operational confidence:
Our client now operates LLM systems with continuous quality validation and clear performance standards. Now the client can easily measure performance on of a model using our LLM Eval solution.
