RAG Systems

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RAG Architecture

1
Introduction to RAG Systems
20 min
FREE
2
Core Components of RAG
22 min
FREE
3
Retrieval Strategies and Techniques
24 min

Building RAG Pipelines

4
Document Processing and Chunking
26 min
5
Embedding Generation and Storage
24 min
6
Implementing the Retrieval Layer
28 min

Advanced RAG Techniques

7
Query Rewriting and Expansion
24 min
8
Reranking and Relevance Scoring
26 min
9
Hybrid RAG: Combining Multiple Approaches
28 min

Production RAG Systems

10
RAG Evaluation and Metrics
24 min
11
Performance Optimization and Caching
26 min
12
Deploying RAG Systems to Production
30 min

Introduction to RAG Systems

Welcome to RAG Systems! This course will teach you how to build Retrieval Augmented Generation systems - one of the most powerful architectures for creating AI applications that can access and reason over custom knowledge bases.

What is RAG?

Retrieval Augmented Generation (RAG) is an architectural pattern that enhances Large Language Models by retrieving relevant information from external knowledge sources before generating responses. Instead of relying solely on the model's training data, RAG systems dynamically fetch contextual information to provide more accurate, up-to-date, and grounded answers.

Why RAG Matters

RAG solves critical limitations of standalone LLMs:

  • Knowledge Cutoff - Access information beyond the model's training date
  • Hallucinations - Ground responses in retrieved facts, reducing false information
  • Domain Specificity - Provide specialized knowledge without fine-tuning
  • Transparency - Show sources and citations for generated content
  • Privacy - Keep sensitive data in your control, not in the model
  • Cost Efficiency - Avoid expensive fine-tuning for domain adaptation

Core Components of RAG

A typical RAG system consists of several key components:

  • Document Processing - Loading, cleaning, and chunking documents
  • Embedding Model - Converting text into vector representations
  • Vector Store - Storing and indexing embeddings for fast retrieval
  • Retriever - Finding relevant documents based on queries
  • LLM - Generating responses using retrieved context
  • Orchestrator - Coordinating the entire pipeline

How RAG Works

The RAG process follows these steps:

  1. Indexing Phase - Documents are processed, embedded, and stored in a vector database
  2. Query Time - User question is converted to an embedding
  3. Retrieval - Most relevant documents are retrieved based on similarity
  4. Augmentation - Retrieved context is combined with the user query
  5. Generation - LLM generates a response using the augmented prompt

RAG Variants and Patterns

The RAG ecosystem includes several advanced patterns:

  • Naive RAG - Basic retrieve-then-generate approach
  • Advanced RAG - With query rewriting, hybrid search, and reranking
  • Modular RAG - Combining multiple retrieval strategies
  • Agentic RAG - Using AI agents to orchestrate retrieval
  • Self-RAG - Models that reflect on retrieval quality
  • CRAG - Corrective RAG with self-assessment

Real-World Applications

RAG powers numerous production applications:

  • Customer Support - AI chatbots with access to company knowledge
  • Documentation Search - Intelligent technical documentation assistants
  • Legal Research - Finding relevant cases and regulations
  • Medical Diagnosis - Supporting doctors with medical literature
  • Financial Analysis - Analyzing reports and market data
  • Research Assistants - Helping researchers find relevant papers
  • Enterprise Search - Semantic search across company documents

Popular RAG Frameworks

Several frameworks simplify RAG development:

  • LangChain - Comprehensive framework with extensive integrations
  • LlamaIndex - Specialized for data ingestion and indexing
  • Haystack - Production-ready NLP framework
  • Semantic Kernel - Microsoft's AI orchestration framework
  • AutoGen - Multi-agent conversation framework

What You'll Learn

This comprehensive course covers:

  • RAG architecture and design patterns
  • Document processing, chunking strategies, and metadata
  • Building retrieval pipelines with vector databases
  • Advanced techniques: query rewriting, reranking, hybrid search
  • Evaluating RAG systems with proper metrics
  • Optimization strategies for production deployments
  • Handling edge cases and error scenarios
  • Building end-to-end RAG applications

Prerequisites

  • Understanding of Large Language Models
  • Knowledge of vector databases and embeddings
  • Python programming experience
  • Familiarity with APIs and web development basics

By the end of this course, you'll be able to design and deploy sophisticated RAG systems that provide accurate, grounded, and contextually relevant AI responses.

Let's build powerful RAG systems!