RAG & Knowledge Bases
Build RAG systems from scratch. Master document chunking, embeddings, vector databases, hybrid search, reranking, and evaluation for production knowledge bases.
LLMs are powerful, but they have a fundamental limitation: they only know what they were trained on. Ask about your company’s internal docs, last quarter’s data, or anything after the training cutoff, and you’ll get confident-sounding nonsense.
RAG (Retrieval-Augmented Generation) fixes this by feeding the LLM relevant information at query time — from your documents, databases, or knowledge bases. It’s how most enterprise AI products actually work. But the gap between “basic RAG that kind of works” and “production RAG that’s reliable” is bigger than most tutorials let on.
This course bridges that gap.
You’ll build a RAG system from the ground up: document processing with proper chunking strategies, embedding models and vector databases for storage and retrieval, and generation pipelines with grounding and citation controls that reduce hallucination.
Each lesson goes beyond the basics. You’ll learn hybrid search (combining keyword and semantic), reranking for better results, query rewriting for tricky questions, and the RAGAS evaluation framework that tells you whether your system is actually working or just looks like it is.
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
After This Course, You Can
What You'll Build
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
Who Is This For?
- Backend developers building features that need to answer questions from company documents or knowledge bases
- AI engineers who've built basic RAG and want to improve retrieval accuracy and reduce hallucination
- Product managers evaluating RAG for their product who need to understand what's technically involved
- Anyone who's tried ChatGPT with file upload and thought "I want this but better, faster, and on my own data"
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.
