The Architecture of RAG Systems Part 01

Описание: In this lecture, we explore Retrieval-Augmented Generation, or RAG, as a full AI systems architecture rather than just a popular buzzword. The session explains why standalone large language models are not enough for many real-world applications, especially when answers must be grounded in current, private, or domain-specific knowledge.

We walk through the complete RAG pipeline step by step: data ingestion, chunking, embedding generation, vector databases, retrieval flow, augmented prompt construction, hybrid retrieval, evaluation, debugging, and system limitations. The lecture also shows how RAG fits into the broader evolution of modern AI systems and why it serves as a foundation for the move toward agentic RAG.

To keep the discussion practical, the lecture uses a recurring engineering example based on a corporate document collection, showing how a system can retrieve relevant evidence and generate grounded answers from real sources.

This lecture is designed for students, engineers, and practitioners who want a clear architectural understanding of how RAG systems work in production settings.

Topics covered

Why RAG matters

Offline and online RAG pipelines

Data ingestion and chunking strategies

Embeddings and vector databases

Dense, lexical, and hybrid retrieval

Augmented prompt design

Evaluation metrics and debugging

Strengths and limitations of RAG

Transition from traditional RAG to agentic RAG

This session is part of a broader course on modern AI systems architecture.

#RAG #AI #ArtificialIntelligence #LLM #GenerativeAI #MachineLearning #VectorDatabase #SemanticSearch #AgenticAI #AIEngineering