Comprehensive RAG Learning Resource

Understand RAG. Build Smarter AI.

Learn how AI retrieves knowledge, searches documents, and generates grounded answers. From fundamentals to advanced architectures.

Start Learning Explore RAG Architectures

How RAG Works

Click each stage to learn how documents become AI-powered answers.

Click any stage to learn more about it

What You'll Learn

A complete learning roadmap from RAG basics to production architectures.

In-Depth Lessons

Detailed explanations with diagrams, code examples, and real-world scenarios.

Interactive Pipeline

Click through the RAG pipeline stages to understand each component.

15+ Tools Covered

Reference pages for LangChain, ChromaDB, FAISS, Neo4j, Ollama, and more.

Practical Projects

Step-by-step tutorials to build Chat with PDF, Agentic RAG, and more.

Accurate Content

Technically accurate explanations that distinguish architecture from hype.

No Login Required

All educational content is freely available without API keys or accounts.

Interactive Architecture Simulator

Eight Animated RAG Architectures

Watch live simulated queries and payloads travel across all 8 architectural patterns. Toggle playback, adjust speed, and inspect intermediate token payloads.

Interactive Animated ArchitectureBeginner

Basic / Naive RAG

Standard sequential retrieval and prompt augment pipeline

Step 1

Ingestion Phase

Source PDF — Document Ingestion

Raw PDF uploaded by student (e.g., "Cloud Computing Architecture Guide.pdf").

Under The Hood:

Parsed using PyPDFLoader or Unstructured to extract raw text, table structures, and page metadata.

Industry Tools:PyPDFpdfplumberUnstructured

Document Metadata

LIVE PAYLOAD

{ "filename": "Cloud_Computing_Guide.pdf", "pages": 42, "file_size": "2.4 MB", "parser": "PyPDFLoader" }

Basic / Naive RAG

The foundational pattern: load, chunk, embed, retrieve, and generate.

Conversational RAG

Multi-turn conversations with chat history and follow-up resolution.

Hybrid RAG

Combine keyword (BM25) and dense vector search for better retrieval.

Agentic RAG

AI agents that decide when and how to retrieve, using tools and workflows.

Corrective RAG

Evaluate retrieval quality and take corrective actions when needed.

Self-RAG

Self-reflective generation with retrieval and answer quality evaluation.

GraphRAG

Knowledge graphs for relationship-aware, structured retrieval.

Multimodal RAG

Handle text, images, tables, and charts from mixed-media documents.

Tools & Frameworks

Explore the ecosystem of tools that power RAG systems.

LangChainLlamaIndexChromaDBFAISSQdrantWeaviatePineconeNeo4jElasticsearchOllama

View All Tools

Build Real Projects

Go from theory to practice with step-by-step project tutorials including Chat with PDF, Agentic RAG, Hybrid Search, and more.

Explore Projects

Frequently Asked Questions

What is RAG?

Retrieval-Augmented Generation (RAG) is an architecture pattern that enhances LLM responses by retrieving relevant information from external knowledge sources before generating an answer. This helps reduce hallucination and provides up-to-date, grounded responses.

Do I need programming experience?

Basic Python knowledge is helpful for the code examples and projects. The conceptual explanations are written to be accessible to anyone with an interest in AI and machine learning.

Is RAG better than fine-tuning?

Neither is universally better. RAG excels at providing dynamic, up-to-date knowledge with source citations. Fine-tuning is better for adapting model behavior and style. Many production systems use both approaches together.

What tools do I need to get started?

To start learning, you only need this website. For hands-on projects, you'll need Python, a framework like LangChain or LlamaIndex, and a vector database like ChromaDB. All tools are covered in our Tools section.

How is this different from other RAG resources?

This hub covers eight distinct RAG approaches with clear distinctions between them, interactive diagrams, practical projects, and an honest classification that avoids conflating architectures with components.