Build Modern AI Applications with Vector Databases and Retrieval-Augmented Generation (RAG) Have you ever wondered how ChatGPT, Claude, Gemini, and modern AI assistants search millions of documents and answer questions using your own data? The answer is Vector Databases and Retrieval-Augmented Generation (RAG). In this hands-on course, you’ll learn how to build AI-powered Semantic Search applications and production-ready RAG systems completely from scratch using Python. Instead of focusing only on theory, you’ll build real-world AI applications while learning the concepts behind embeddings, semantic search, vector databases, and modern AI architectures.
Throughout the course you’ll work with industry-standard technologies including:
- Python
- OpenAI Embeddings
- ChromaDB
- Pinecone
- LangChain
- Semantic Search
- Vector Search
- RAG
- PDF Processing
You’ll begin by understanding how embeddings represent the meaning of text and why semantic search is far more powerful than traditional keyword search. Next, you’ll learn how to generate embeddings using the OpenAI API, compare vectors using cosine similarity, and build your own searchable knowledge base. From there, you’ll build a complete Semantic PDF Search Engine capable of searching documents using natural language.
