Vector Databases for Generative AI: Build RAG Apps in 48 Hours (Data Engineering & Governance Series)

Vector Databases for Generative AI: Build RAG Apps in 48 Hours Super-charge your Large Language Model projects with the missing layer every Gen AI system needs— vector databases . In just two days of hands-on labs, you’ll master Pinecone, Milvus, pgvector, LangChain and more, and deploy a production-ready Retrieval-Augmented Generation (RAG) chatbot that actually cites your data.

What you’ll learn The essential theory—embeddings, cosine similarity, ANN indexes (HNSW, IVF) and hybrid search. Cloud vs. self-hosted workflows: Pinecone’s serverless ease, Milvus performance tuning, PostgreSQL + pgvector simplicity. Step-by-step RAG pipeline with LangChain & OpenAI—index, retrieve, generate, cite. Enterprise chatbots on Azure AI Search & Amazon Kendra with zero-infrastructure ingestion pipelines. Cost-saving playbook: hot/cold tiers, lakehouse integration and scaling tricks that cut bills by 60%. 10 real lab exercises and 4 industry case studies (e-commerce, legal, support, IoT) to cement skills. Whether you’re a data engineer, software architect or curious builder, this concise, senior-level guide gives you everything needed to unlock semantic search and grounded AI now —no prior vector experience required.

Stop hallucinations, start answering with facts. Build your first RAG app this weekend—and stay ahead in the AI race.