@hackproduct9: Most RAG demos stop at: Chunk → Embed → Retrieve → Prompt. Production RAG starts after that. 🧠⚙️ Think of it like a repo: 📁 01_offline_build_time/ Parse → chunk → embed → index 📁 02_online_query_time/ Rewrite → retrieve → rerank → assemble → generate 📁 03_knowledge_store/ Vectors + metadata + raw docs + freshness 📁 04_retrieval_quality/ Hybrid search + filters + top-k + reranking 📁 05_answer_quality/ Grounding + citations + structured output + fallback 📁 06_state_data_layer/ Postgres + Redis + object storage + vector index 📁 07_observability_evals/ Latency + recall + answer quality + hallucinations + cost 📁 08_safety_operations/ ACLs + PII protection + prompt injection defense + caching + feedback That’s the mental shift: RAG is not a vector database feature. It’s a production system. The hard part isn’t generating embeddings. The hard part is making retrieval fresh, relevant, secure, observable, and reliable every single time. Save this before your next AI system design interview. 🚀 #RAG #AIEngineering #LLM #GenerativeAI #VectorDatabase
