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LLM Engineering & RAG Systems
Build production-grade Retrieval-Augmented Generation systems from scratch — mastering embeddings, vector databases, chunking strategies, retrieval pipelines, and evaluation frameworks used by top AI engineering teams.
4 sections·12 lessons
What you'll learn
1. Foundations of RAG Architecture
- Why RAG Exists: The Knowledge Problem
- The RAG Pipeline: A Visual Overview
- RAG System Components Deep Dive
2. Embeddings & Vector Databases
- Understanding Embeddings: From Words to Vectors
- Vector Space: How Meaning Becomes Geometry
- Vector Databases: Choosing and Using the Right Store
3. Chunking Strategies & Retrieval Engineering
- Chunking: The Art of Splitting Documents
- Advanced Retrieval: Beyond Naive Top-K
- Retrieval Pipeline: From Query to Context
4. Evaluation, Production & Best Practices
- Evaluating RAG Systems: Metrics That Matter
- Knowledge Check: RAG Systems Mastery
- Production RAG: Guardrails, Observability & Cost
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