RAG From Scratch: Chunking, Embedding, Retrieval, and Evaluation
Build a production-ready Retrieval-Augmented Generation (RAG) system from scratch. Learn chunking strategies, embedding models, vector search, re-ranking, and evaluation. Free intermediate course.
Course Content
RAG From Scratch
Every serious LLM application in 2026 uses RAG. This course teaches you to build one that works in production — not just as a demo.
What is RAG?
Retrieval-Augmented Generation combines the reasoning power of large language models with the factual accuracy of search. Instead of relying on what the model memorized during training, RAG retrieves relevant documents from your own knowledge base and uses them to answer questions — grounding every response in your data.
RAG is the dominant architecture for enterprise AI because it’s:
- Accurate: Answers are grounded in your documents, not model hallucinations
- Updatable: Add new documents without retraining
- Auditable: You can trace every answer back to its source
What You’ll Build
- Personal document Q&A: Ask questions about any PDF, get cited answers
- Enterprise knowledge base: Multi-document RAG with access control and evaluation
- Production API: FastAPI service with sub-second retrieval latency
Connection to SuperML Products
DataTruth — SuperML’s open source analytics product — uses vector retrieval for natural language data access. This course teaches the same retrieval stack DataTruth is built on.
📋 Prerequisites
- Python programming (intermediate level)
- Familiarity with LLM APIs (OpenAI, Anthropic, or similar)
- Basic understanding of what vector embeddings are
🎯 What You'll Learn
- Design and implement a complete RAG pipeline from document ingestion to answer generation
- Choose the right chunking strategy for any document type
- Set up and query vector databases including ChromaDB, Pinecone, and pgvector
- Implement hybrid retrieval and cross-encoder re-ranking for better precision
- Evaluate RAG systems rigorously using RAGAS and custom metrics