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LLM and RAG Evaluation. Python Libraries for LLM and RAG Evaluation. - deepeval: https://lnkd.in/gXCFJaHb - ragas: https://lnkd.in/gJyn7jYj #LLMs #RAG #llmeval #python
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Everyone can build RAG systems. But evaluating LLMs and RAG Systems is very tricky. Here is my updated guide on common metrics and ways to evaluate LLM based applications compiled from various resources including Ragas and DeepEval. This includes: - Statistical Metrics - Model-based Metrics - LLM-based Scorers - RAG and LLM response evaluation metrics - Special focus on metrics like faithfulness, relevancy, precision, recall The content has been made using articles and documentation from DeepEval and Ragas which have a wealth of knowledge. I have curated the information in a concise manner for quick reference. High-resolution version of the same document is below. Do share with others if useful!
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Evaluating a RAG & LLM system can be difficult. Here is a guide about how to evaluate this type of systems.
Head of Community • Principal AI Scientist • Google Developer Expert & Cloud Champion Innovator • Author
Everyone can build RAG systems. But evaluating LLMs and RAG Systems is very tricky. Here is my updated guide on common metrics and ways to evaluate LLM based applications compiled from various resources including Ragas and DeepEval. This includes: - Statistical Metrics - Model-based Metrics - LLM-based Scorers - RAG and LLM response evaluation metrics - Special focus on metrics like faithfulness, relevancy, precision, recall The content has been made using articles and documentation from DeepEval and Ragas which have a wealth of knowledge. I have curated the information in a concise manner for quick reference. High-resolution version of the same document is below. Do share with others if useful!
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✨ Evaluate RAG #Agents automatically 🪄 RAGET (RAG Evaluation Toolkit) can automate and enhance your RAG model's evaluation process. It creates a comprehensive test set, complete with questions, reference answers, and contexts tailored to your model's knowledge base. The toolkit evaluates key components of your RAG agent: the generator, retriever, rewriter, router, and knowledge base. You'll be able to: ‣ Get diagnostics with realistic questions that target specific elements of your RAG system. ‣ Identify potential weaknesses in your RAG components with its detailed question-to-component mapping. ‣ Evaluate your agent’s answers against reference answers, thanks to a LLM-as-a-judge approach ‣ Gain insights into your RAG agent's performance and quickly pinpoint areas for improvement. Learn more 👉 https://gisk.ar/4dh26oH #RAG #LLMs #LLMeval #RAGevaluation
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Purpose: In the Final Case Analysis, you will analyze a case study and use gradu
Purpose: In the Final Case Analysis, you will analyze a case study and use gradu
