Llm Evaluation

What it does

This skill, hermeticormus-llm-evaluation, provides automated evaluation of Large Language Model (LLM) outputs. It assesses responses based on pre-defined criteria and generates structured reports with scores for various aspects like helpfulness, accuracy, and safety. The tool can be used to benchmark LLMs or identify areas where a model needs improvement.

When to use it

• Benchmarking different LLMs: Compare the performance of several models on a standardized set of prompts and evaluation criteria.
• Evaluating prompt engineering efforts: Assess how changes to prompts impact the quality and safety of LLM responses.
• Identifying model weaknesses: Pinpoint areas where an LLM consistently struggles, such as factual accuracy or avoiding harmful content.
• Automated regression testing: Ensure that updates to an LLM don’t degrade its performance on critical tasks.

Key capabilities

• Automated evaluation of LLM responses
• Scoring based on predefined criteria (helpfulness, accuracy, safety)
• Generation of structured reports with scores
• Benchmarking across multiple models

Example prompts

• "Evaluate the following response from an LLM: [LLM Response] against these criteria: helpfulness, accuracy, and safety."
• "Compare the responses to this prompt '[Prompt]' between Model A and Model B, focusing on factual correctness."
• "Run a regression test on the model's ability to answer questions about historical events. Provide a detailed report of scores."

Tips & gotchas

• The quality of evaluation depends heavily on the clarity and specificity of the criteria provided. Well-defined scoring rubrics are essential for reliable results.