May 14
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In this video, we start building a real-world RAG system from scratch and understand how to design, implement, and evaluate it properly. We cover complete architecture, use cases, planning, and evaluation metrics like faithfulness, answer relevancy, context precision, and recall. We also follow an industry-level approach where every component is selected based on alternatives and proper justification. Reference Notes GitHub Repo https://github.com/switch2ai 🧠 Interview Mindset (VERY IMPORTANT) For Each Component Which method used in project? What are alternatives? (which you experimented?) Why that method is chosen? 👉 Always explain like this Start with alternatives → then final choice → then justification 🧠 Problem Statements ============ Static Document ============ Admin → Upload document User → Ask questions 🔥 Use Cases HR Policy Chatbot Admin → HR Team User → Employees Customer Chatbot Admin → Company (FAQs, manuals, policies) User → Customers Insurance Chatbot Admin → Insurance Company User → Agents ============ User Uploaded Document ============ User → Upload + Ask 🔥 Use Cases Financial Analysis Upload → 10K / 10Q reports Legal Chatbot Upload → Contracts Medical Chatbot Upload → Reports 🏗️ Architecture 🔹 Admin Flow Upload document → Text Extraction → Chunking → Embedding → VectorDB 🔹 User Flow Query → Retriever → Relevant chunks → Model → Answer 🧠 Planning Data Format PDF, Word, CSV, Tables Scanned PDF Images Handwritten Text Security Data Security Prompt Injection Hallucination Handling Sensitive Data (PII / PHI) Infrastructure CPU / GPU RAM Storage Cost ⚙️ Component-wise Explanation (IMPORTANT) 1️⃣ Document Loader Alternatives PyPDFLoader Unstructured OCR Used PyPDFLoader Why Fast + simple for structured PDFs 2️⃣ Chunking Alternatives Character splitter Recursive splitter Semantic chunking Used RecursiveCharacterTextSplitter Why Maintains context + better chunk quality 3️⃣ Embedding Model Alternatives OpenAI embeddings Sentence Transformers Cohere embeddings Used OpenAI Embeddings Why High accuracy + easy integration 4️⃣ Vector Database Alternatives Chroma FAISS Pinecone Weaviate Used Chroma Why Lightweight + local + easy setup 5️⃣ Retriever Alternatives Similarity MMR Hybrid Used Similarity Search Why Simple + fast baseline 6️⃣ LLM Alternatives OpenAI Claude Llama Used OpenAI Why Best performance + stable 📊 RAG Evaluation 👉 RAG = Retriever + Generator 🔹 Retriever Metrics Context Precision Context Recall 🔹 Generator Metrics Faithfulness Answer Relevancy 🔥 Faithfulness Answer should be factually correct based on context 2 correct claims → score = 1 1 correct → score = 0.5 🔥 Answer Relevancy Check if answer matches question Steps Generate questions from answer Compare with original question Use cosine similarity 🔥 Context Recall Did retriever fetch all required info All correct → score = 1 Missing → lower score 🔥 Context Precision Relevant facts / total retrieved facts 🧪 Evaluation Data Question Answer Context Ground Truth ⚙️ Evaluation Pipeline Retriever → Context LLM → Answer 🧪 RAGAS Evaluation Metrics Faithfulness Answer Relevancy Context Precision Context Recall 🔄 Synthetic Data Generation Used when GT is not available Helps automate evaluation 🚀 Key Takeaways Always justify every component Retriever + Generator both matter Accuracy alone is not enough Use RAGAS for proper evaluation 🔥 Hashtags #RAG #GenAI #LangChain #AI #MachineLearning #DeepLearning #DataScience #RAGProject #RAGAS #Switch2AI 🔍 SEO Tags rag project end to end rag system design tutorial rag evaluation metrics explained context precision recall rag faithfulness answer relevancy rag ragas tutorial genai rag project langchain rag project rag architecture explained rag chatbot project 🔍 SEO Tags (500 char) rag project end to end,rag system design tutorial,rag evaluation metrics explained,context precision recall rag,faithfulness answer relevancy rag,ragas tutorial,genai rag project,langchain rag project,rag architecture explained,rag chatbot project,rag system implementation,Switch 2 AI