Скачать или смотреть RAG Systems Explained: Complete Guide from Theory to Implementation

Learn how to build powerful Retrieval-Augmented Generation (RAG) systems from scratch — from understanding the core concepts to writing production-ready code.
This step-by-step tutorial covers architecture, embeddings, vector databases, chunking, retrieval, ranking, and integrating everything with an LLM.

Perfect for AI engineers, developers, and anyone building real-world GenAI apps.

What you’ll learn:

What RAG is and why it's essential for accurate LLM responses

RAG architecture explained (simple + advanced flow)

How embeddings & vector search work

Chunking, splitting & preprocessing best practices

Building a retrieval pipeline

Adding reranking for higher accuracy

Implementing RAG with Python

Integrating with popular vector DBs (FAISS, Chroma, Pinecone)

When to use RAG vs fine-tuning

📌 Code examples included
📌 Beginner-friendly + practical
📌 Covers 2025 best practices