RAG & Knowledge Bases
Build RAG systems from scratch. Master document chunking, embeddings, vector databases, hybrid search, reranking, and evaluation for production knowledge bases.
What You'll Learn
- Explain how RAG solves LLM limitations like hallucination and knowledge cutoffs
- Implement document processing pipelines with appropriate chunking and overlap strategies
- Compare embedding models and vector databases to select the right tools for each use case
- Design retrieval strategies using hybrid search, reranking, and query rewriting
- Build generation pipelines with grounding, citation, and faithfulness controls
- Evaluate RAG systems using RAGAS metrics: faithfulness, context relevance, and answer relevance
Course Syllabus
Prerequisites
- Basic understanding of AI and LLMs (our AI Fundamentals course recommended)
- Familiarity with APIs and data formats (JSON, CSV)
- No coding required for concepts — optional exercises use Python
Frequently Asked Questions
Do I need to know how to code to take this course?
No. The core concepts (architecture, chunking strategies, retrieval patterns) are explained without code. Optional exercises use Python, but you'll understand RAG systems even without them.
What's the difference between RAG and fine-tuning?
RAG retrieves relevant information at query time from external documents. Fine-tuning permanently changes the model's weights with new training data. This course covers RAG because it's faster to implement, easier to update, and doesn't require ML infrastructure.
Which vector database does this course teach?
This course is database-agnostic. You'll learn the principles that apply to Pinecone, Weaviate, Qdrant, Chroma, pgvector, and any vector database. We compare them so you can choose the right one.
Can I use RAG with any LLM?
Yes. RAG works with Claude, ChatGPT, Gemini, Llama, Mistral, and any other LLM. The retrieval pipeline is model-independent — you retrieve relevant context and feed it to whatever LLM you use.