@datascibykashi: Traditional RAG retrieves documents and generates an answer. Agentic RAG goes further. Instead of following one fixed retrieval pipeline, an AI agent can reason, decide which tools to use, retrieve information, evaluate results, and iterate before producing the final answer. 🧩 THE ARCHITECTURE 1️⃣ USER QUERY ↓ User asks a question 2️⃣ AI AGENT ↓ Understands the task and decides what to do 3️⃣ QUERY PLANNER ↓ Breaks complex questions into smaller retrieval tasks 4️⃣ RETRIEVAL ↓ Searches your knowledge base using: • Vector search • Keyword search • Hybrid search • Metadata filtering 5️⃣ RERANKING ↓ Ranks retrieved documents by relevance 6️⃣ CONTEXT EVALUATION ↓ Agent checks: • Is the context relevant? • Is information sufficient? • Are sources trustworthy? 7️⃣ TOOL CALLING ↓ If required, the agent can use: • Web search • SQL databases • APIs • Calculators • Python tools • Internal knowledge bases 8️⃣ ITERATIVE RETRIEVAL ↓ Not enough information? Search again. 🔄 The agent can reformulate the query and retrieve additional context. 9️⃣ GENERATION ↓ LLM generates the final response using the selected evidence. 🔟 CITATIONS + VALIDATION ↓ Return the answer with supporting sources and verify that claims are grounded. 🏗️ TECH STACK LLM: GPT / Claude / Gemini / open-source models Orchestration: LangGraph / LangChain Embeddings: Sentence Transformers / OpenAI embeddings Vector DB: Qdrant / Pinecone / Weaviate / Chroma Reranker: Cross-encoder / Cohere / BGE Backend: FastAPI Database: PostgreSQL Frontend: React / Streamlit Deployment: Docker + Cloud 🔥 BASIC AGENTIC RAG FLOW Question → Agent → Plan → Retrieve → Rerank → Evaluate → Tool call if needed → Retrieve again if necessary → Generate → Verify → Final Answer + Sources 🧠 WHAT MAKES IT “AGENTIC”? Traditional RAG: Query → Retrieve → Generate Agentic RAG: Query → Reason → Plan → Act → Retrieve → Evaluate → Iterate → Generate The key difference is decision-making and feedback loops, not simply adding an LLM to a RAG pipeline. 💡 PROJECT IDEA Build a Research Assistant Agent that can: 📄 Search research papers 🔎 Retrieve relevant sections 🧠 Break complex questions into sub-questions 🌐 Search external sources when necessary 📊 Analyze structured data 🔗 Track citations ✅ Verify retrieved evidence 💬 Generate a grounded final answer That is much closer to a production-grade AI system than a basic chatbot. 📌 Save this architecture if you’re learning RAG or AI Engineering. #AgenticRAG #RAG #GenerativeAI #creatorsearchinsights #machinelearningengineer

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Friday 18 September 2026 17:19:05 GMT