Llm Evaluation

What it does

The microck-llm-evaluation skill provides a framework for evaluating Large Language Models (LLMs). It allows users to define evaluation criteria and prompts, then assess LLM responses based on those defined parameters. The skill aims to provide structured feedback on LLM performance, enabling iterative improvement of model behavior.

When to use it

• Benchmarking new models: Compare the performance of different LLMs on a specific task or dataset.
• Evaluating prompt engineering efforts: Determine if changes to prompts are improving LLM output quality.
• Identifying failure modes: Pinpoint areas where an LLM consistently struggles or produces undesirable responses.
• Measuring improvements after fine-tuning: Quantify the impact of fine-tuning on a specific model's capabilities.

Key capabilities

• Define evaluation criteria (e.g., accuracy, relevance, safety).
• Create and manage prompts for LLM evaluation.
• Assess LLM responses against defined criteria.
• Provide structured feedback on LLM performance.

Example prompts

• "Evaluate the following response to the prompt: '[Prompt text]' using these criteria: [Criteria list]"
• "Run an evaluation of model X on this set of prompts: [List of prompts]"
• "Compare the responses from Model A and Model B to the prompt 'Summarize this article:' [Article Text] based on accuracy."

Tips & gotchas

The skill's effectiveness relies heavily on clearly defined evaluation criteria. Ambiguous or poorly designed criteria will lead to unreliable results.