eval-frameworks

What problem does it solve?

This Skill addresses the challenge of objectively measuring the quality of LLM-generated text, particularly in RAG (Retrieval Augmented Generation) systems, by providing frameworks for evaluating faithfulness, relevance, and other critical metrics.

Core Features & Use Cases

• Faithfulness Metrics: Assess if LLM answers are factually supported by the provided context, detecting hallucinations.
• LLM-as-a-Judge: Utilize powerful LLMs to evaluate the quality of responses based on custom criteria (e.g., professionalism, relevance).
• Synthetic Data Generation: Create automated test cases for benchmarking and regression testing when manual data is scarce.
• Use Case: Ensure your RAG chatbot's answers are always grounded in the documentation it retrieves, preventing the spread of misinformation.

Quick Start

Use the eval-frameworks skill to evaluate the faithfulness of a given response against its context.