I Built a RAG Evaluation System with RAGAS + Groq

Description

Learn how to build AND evaluate a production-ready RAG pipeline using RAGAS metrics, Groq LLMs, and local embeddings. This Google Colab notebook shows you exactly how to measure faithfulness, context precision, answer correctness, and context recall. github - https://github.com/siddharth-Kharche/RAG-RAGAS-Evaluation- 🚀 What You'll Build: ✅ Local FAISS vector database for semantic search ✅ RAG pipeline powered by Groq's LLAMA-3 models ✅ Complete RAGAS evaluation with 4 key metrics ✅ Sentence-transformers for local embeddings ✅ Production-ready evaluation framework 📊 RAGAS Metrics Covered: Faithfulness: Does the answer stick to retrieved context? Answer Correctness: How accurate is the response? Context Precision: Did we retrieve the right documents? Context Recall: Did we retrieve ALL relevant documents? ⚙️ Tech Stack: Groq API (LLAMA-3.1 & LLAMA-3.3) FAISS for vector search Sentence-transformers (all-mpnet-base-v2) LangChain for RAG orchestration RAGAS for evaluation metrics 💻 Free Google Colab Notebook: [Link to your Colab notebook] 🔗 GitHub Repository: [Your GitHub repo link] 📚 Resources Mentioned: RAGAS Documentation Groq API Setup Guide RAG Best Practices 2025 🎯 Perfect For: AI/ML developers building RAG systems Data scientists evaluating LLM applications Engineers working with vector databases Anyone wanting to measure RAG quality objectively 💡 Why RAGAS + Groq? RAGAS is the industry-standard framework for RAG evaluation in 2025, and Groq provides lightning-fast inference at a fraction of OpenAI's cost. This combination gives you production-grade evaluation without breaking the bank.​ 🔔 Subscribe for more AI/ML tutorials on RAG systems, vector databases, and LLM evaluation! #RAG #RAGAS #Groq #MachineLearning #AI RAG evaluation, RAGAS metrics, Groq API, FAISS vector database, RAG pipeline, LLM evaluation, retrieval augmented generation, context precision, faithfulness metric, answer correctness, sentence transformers, local embeddings, LangChain RAG, AI evaluation, machine learning tutorial, vector search, semantic search, Python AI, production RAG, RAG best practices, LLM metrics, Groq LLAMA, AI development, NLP tutorial, RAG system