RAG Evaluation Matrix Day 2: RAGAS Metrics (Faithfulness, Context Precision, Recall)

Description

In this video, we continue RAG Evaluation Matrix Day 2 and learn how to evaluate RAG systems using advanced metrics. We understand how to evaluate both retriever and generator components using metrics like faithfulness, answer relevancy, context precision, and context recall. We also implement evaluation using RAGAS and prepare datasets with question, answer, context, and ground truth. This is essential for building production-ready RAG systems. Reference Notebook Here is the GitHub repo link https://github.com/switch2ai 🧠 RAG Evaluation 🔹 Retriever Metrics Context Precision Context Recall 🔹 Generation Metrics Faithfulness Answer Relevancy 🔥 Faithfulness 👉 Checks if answer is factually correct based on context Example Answer: Einstein was born in Germany in 14th March 1879 Claims Claim 1 → Born in Germany ✔ Claim 2 → Born on 14 March 1879 ✔ Score = 2 / 2 = 1 If one wrong Score = 1 / 2 = 0.5 🔥 Answer Relevancy 👉 Checks if answer is relevant to question 🔹 Steps Generate new questions from LLM response Compare with original question Use cosine similarity Example Original Question Where is France & what is its capital? Model Answer France is in western Europe Generated Questions Where is France located? France is located in which continent? Similarity 0.5, 0.4 Final Score = (0.5 + 0.4) / 2 = 0.45 🔥 Context Recall 👉 How much correct information is retrieved GT France is in western Europe & its capital is Paris Retrieved France is in western Europe ✔ Paris is capital of France ✔ Score = 2 / 2 = 1 If missing one → 1 / 2 = 0.5 🔥 Context Precision 👉 How much retrieved data is relevant Precision = Relevant facts / Total retrieved facts 🔧 RAG Pipeline (Used for Evaluation) Retriever → Context LLM → Answer 📊 Evaluation Data We need Question Answer Context Ground Truth Example Question For what purpose GPU was initially used? Ground Truth GPU was used to simulate human imagination ⚙️ Dataset Structure user_input → Questions ground_truth → Correct answers answer → LLM output retrieved_contexts → Retrieved chunks 🧪 Evaluation using RAGAS Metrics Used Faithfulness Answer Relevancy Context Precision Context Recall Flow Dataset → Metrics → LLM + Embeddings → Score 🔥 Key Takeaways RAG has 2 parts → Retriever + Generator Retriever → Context Precision & Recall Generator → Faithfulness & Relevancy RAGAS → Standard evaluation framework 🚀 Real World Use Production RAG systems Chatbots Enterprise AI Document QA systems 🔥 Hashtags #RAG #RAGAS #AdvancedRAG #GenAI #AI #MachineLearning #DeepLearning #DataScience #LangChain #Switch2AI 🔍 SEO Tags rag evaluation ragas faithfulness answer relevancy explained context precision recall rag rag metrics explained ragas tutorial llm evaluation rag system genai rag evaluation retriever evaluation metrics advanced rag tutorial rag system metrics 🔍 SEO Tags (500 char) rag evaluation ragas,faithfulness answer relevancy explained,context precision recall rag,rag metrics explained,ragas tutorial,llm evaluation rag system,genai rag evaluation,retriever evaluation metrics,advanced rag tutorial,rag system metrics,context recall precision rag,rag evaluation framework,Switch 2 AI