ai-performance-testing

You measure factual accuracy, completeness, and consistency in LLM responses by automating evaluation with the Deepeval framework. It generates test data from documents, configures target LLM systems, executes performance tests, and produces detailed markdown reports.

To generate test data for evaluating an AI system, use the ai-performance-testing skill. It extracts test data directly from your provided documents, configures the target LLM system, and executes performance tests to evaluate factual accuracy, completeness, and consistency.

The target metrics for AI evaluation are over 95% factual accuracy, over 90% completeness, and over 85% consistency. These metrics ensure AI systems provide reliable, correct, and consistent information based on the provided context.

Yes, you can use Deepeval to test a customer support chatbot before launch. It rigorously evaluates the AI's factual accuracy and completeness against provided documents, preventing the dissemination of incorrect information and ensuring a positive user experience.

AI performance test reports are generated in detailed markdown format. These reports contain the evaluation results for factual accuracy, completeness, and consistency metrics after the target LLM system executes the generated test data.

No additional dependencies are required to evaluate LLM performance metrics. The skill operates independently with scripts and references to automate the Deepeval framework evaluation process, requiring only your target documents and LLM system configuration.