RAG: Build Knowledge-Powered AI
Master Retrieval-Augmented Generation — the most impactful AI architecture for enterprise applications. Vector search, chunking, reranking, and production deployment.
About This Course
RAG is how AI becomes useful in the real world — grounded in your actual data, not hallucinating from training data. This course covers the complete RAG stack: document processing, chunking strategies, embedding models, vector stores, retrieval techniques, reranking, and evaluation. You'll build RAG systems on top of Pinecone, Weaviate, and pgvector, and learn why most RAG implementations fail and how to fix them. New to these terms? Reranking is a second-pass scoring step that improves retrieval quality; RAGAS is an open-source RAG evaluation framework; hybrid search combines keyword and semantic search together. All are explained from first principles inside the course. LangChain is used as a practical tool throughout — you will learn what you need as you build. The dedicated LangChain & LangGraph course builds on this foundation for engineers who want to master the framework deeply, including LCEL composition, LangSmith tracing, and LangGraph state machines.
What You'll Learn
- Design robust document ingestion pipelines for any file type
- Implement advanced chunking strategies that preserve semantic meaning
- Compare and select embedding models based on your use case
- Build hybrid search combining vector and keyword retrieval
- Implement reranking with Cohere and cross-encoders for precision
- Evaluate RAG systems with RAGAS and custom metrics
- Optimize retrieval with metadata filtering and query expansion
- Deploy production RAG APIs that handle thousands of queries
Who Is This For?
Building knowledge bases, chatbots, and enterprise search tools
Making internal company knowledge accessible through AI
Implementing RAG for clients across different industries and document types
Prerequisites
- Building AI-Powered Applications with APIs
- Python for AI