ragrank and llm-eval-bench

About ragrank

izam-mohammed/ragrank

🎯 Your free LLM evaluation toolkit helps you assess the accuracy of facts, how well it understands context, its tone, and more. This helps you see how good your LLM applications are.

This toolkit helps you assess the performance of your Retrieval-Augmented Generation (RAG) applications. You provide your RAG model's questions, the contexts it retrieves, and its generated responses, and it gives you metrics on factual accuracy, context understanding, and tone. This is for AI/ML engineers, data scientists, or product managers who build and deploy LLM applications and need to ensure their RAG systems are delivering high-quality, reliable outputs.

About llm-eval-bench

piog/llm-eval-bench

Evaluation harness for prompts, structured outputs, and RAG workflows