Llm Testing

What it does

This skill, yonatangross-llm-testing, provides a framework for evaluating and testing Large Language Models (LLMs). It allows users to define test cases with expected outputs and compare the LLM's responses against those expectations. This facilitates systematic assessment of model performance across various tasks and prompts. The tool aims to improve LLM reliability and identify areas for refinement.

When to use it

• Evaluating new LLMs: Quickly assess a new model’s capabilities before integrating it into a workflow.
• Regression testing: Ensure that changes or updates to an existing LLM don't negatively impact performance on established tasks.
• Prompt engineering validation: Verify that prompt modifications are producing the desired results from the LLM.
• Benchmarking different models: Compare the output quality of multiple LLMs for a specific use case.

Key capabilities

• Test Case Definition: Define test cases with expected outputs.
• Automated Comparison: Automatically compare LLM responses against expected outputs.
• Performance Evaluation: Provides metrics to assess model performance based on comparison results.
• Framework for Systematic Testing: Offers a structured approach to LLM evaluation.

Example prompts

• "Run the 'summarization_test' test case."
• "Evaluate the LLM's response to prompt 'question_answering_prompt' against the expected answer."
• "Show me the results of all tests run in the last 24 hours."

Tips & gotchas

This skill requires a clear understanding of the specific LLM being tested and well-defined test cases with accurate expected outputs. The quality of the testing depends heavily on the precision of these defined expectations.