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llm-evaluation

Evaluate LLM performance, quality, and safety using Python frameworks.

Updated Aug 23, 2026
Search Tags:#llm#evaluation#safety#benchmark#trulens#ragas#deepeval
One-click install
npx skills add https://github.com/badhope/skill --skill llm-evaluation-badhope
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/badhope/skill/tree/main/.trae/skills/domains/ai/llm-evaluation
Command: npx skills add https://github.com/badhope/skill --skill llm-evaluation-badhope

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Systematic evaluation of LLMs for performance, quality, and safety to support reliable model selection and monitoring.

Core Features & Use Cases

  • Quality assessment: quantify factual accuracy, answer relevance, and response consistency.
  • Benchmark execution: run standardized tests (e.g., MMLU, TruthfulQA, HellaSwag) across models and configurations.
  • Safety evaluation: detect unsafe outputs, bias, and safety violations with automated metrics.
  • Use Case: Researchers and engineers comparing model variants, auditing deployments, and benchmarking new services.

Quick Start

Prepare a test dataset and run a baseline evaluation using ragas, deepeval, and trulens to generate a report.

Frequently Asked Questions about llm-evaluation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate LLM performance and safety across different models?▼

To evaluate LLM performance and safety, you run standardized benchmark tests like MMLU and TruthfulQA across model deployments to quantify factual accuracy, response quality, and safety violations using automated metrics.

What metrics are used for RAG evaluation with frameworks like ragas and trulens?▼

RAG evaluation with ragas and trulens uses standardized metrics including faithfulness, answer relevance, context precision, and context recall to quantify factual accuracy and response consistency.

Can I use deepeval to detect bias and safety violations in enterprise AI outputs?▼

Yes, you can use deepeval to perform safety evaluation, detecting unsafe outputs, bias, and safety violations through automated metrics within enterprise AI workflows.

What is the best way to benchmark factual accuracy across multiple LLM deployments?▼

The best way to benchmark factual accuracy is by applying configurable test datasets across multiple models using Python frameworks to generate comparative reports on factual accuracy and response consistency.

Do I need a specific test dataset to start benchmarking LLM quality?▼

Yes, you need to prepare a configurable test dataset to run a baseline evaluation and generate a standardized report on factual accuracy, relevance, and safety indicators.

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