RAG Systems: Complete Learning Path

import { Card, CardGrid, LinkCard, Aside, Steps } from ‘@astrojs/starlight/components’;

Retrieval-Augmented Generation (RAG) is one of the most powerful patterns for building AI applications that work with your own data. This learning path will take you from understanding the basics to implementing production-ready RAG systems.

What is RAG?

RAG combines the power of large language models with the ability to retrieve relevant information from your documents. Instead of relying solely on the model’s training data, RAG systems:

• Retrieve relevant information from your document collection
• Augment the LLM prompt with this context
• Generate accurate, contextual responses based on your data

RAG enables LLMs to work with: - Private company knowledge bases - Up-to-date information beyond training cutoff - Domain-specific documentation - Large document collections

All without expensive model fine-tuning.

Learning Path

Follow these guides in order to build your RAG expertise:

**Level**: Beginner to Intermediate **Time**: 45-60 minutes

Start here if you're new to RAG. Learn the fundamentals by building a complete RAG system with LangChain, OpenAI embeddings, and ChromaDB.

**What you'll learn**:
- Core RAG architecture
- Document loading and chunking
- Creating embeddings
- Vector store setup
- Retrieval chains

<LinkCard
  href="/developers/rag-systems/building-first-rag-system"
  title="Start Building →"
  description="Step-by-step tutorial with working code"
/>

**Level**: Intermediate **Time**: 50-65 minutes

Deep dive into embeddings and search techniques. Learn how semantic search works and how to implement efficient retrieval systems.

**What you'll learn**:
- Creating and processing embeddings
- Text chunking strategies
- Vector similarity search
- Metadata filtering

<LinkCard
  href="/developers/rag-systems/embedding-search-systems"
  title="Learn Embeddings →"
  description="Master document processing and chunking"
/>

**Level**: Intermediate **Time**: 60-75 minutes

Complete guide to vector databases and semantic search. Compare different vector stores (Pinecone, Weaviate, ChromaDB, Qdrant) and learn advanced search techniques.

**What you'll learn**:
- Vector database comparison
- Similarity metrics
- Hybrid search (semantic + keyword)
- Re-ranking with cross-encoders
- Production best practices

<LinkCard
  href="/developers/rag-systems/vectorization-semantic-search"
  title="Master Vector Search →"
  description="Embeddings, vector databases, and similarity metrics"
/>

**Level**: Advanced **Time**: Variable

Take your RAG system to the next level with advanced patterns like hybrid search, query expansion, HyDE, and contextual compression.

**What you'll learn**:
- Hybrid search implementation
- Cross-encoder re-ranking
- Query expansion strategies
- Hypothetical Document Embeddings (HyDE)
- Contextual compression

<LinkCard
  href="/developers/rag-systems/advanced-rag-techniques"
  title="Go Advanced →"
  description="Production-grade RAG patterns"
/>

Quick Start Roadmap

1. Start with the basics (1-2 days)

   Begin with Building Your First RAG System. Set up a working prototype and understand the core concepts.

2. Deep dive into embeddings (2-3 days)

   Study Embedding & Search Systems and Vectorization & Semantic Search to understand the retrieval layer.

3. Optimize and scale (1 week)

   Implement Advanced RAG Techniques to improve retrieval quality and prepare for production.

4. Build your application (ongoing)

   Apply these patterns to your specific use case, iterating based on evaluation metrics.

Common RAG Use Cases

RAG systems excel at:

• Internal Knowledge Bases: Answer questions from company documentation
• Customer Support: Provide accurate responses based on help docs
• Research Assistants: Search and synthesize information from papers
• Legal/Compliance: Query regulations and case law
• Code Documentation: Search codebases and technical docs
• Medical Information: Retrieve relevant medical literature

Key Technologies

Throughout this learning path, you’ll work with:

• Embedding Models: OpenAI text-embedding-3-small/large, Sentence Transformers
• Vector Databases: ChromaDB, Pinecone, Weaviate, Qdrant, FAISS
• Frameworks: LangChain, LlamaIndex
• LLMs: OpenAI GPT-4, Claude, open-source models
• Search Techniques: Semantic search, hybrid search, re-ranking

Prerequisites

Before starting this learning path, you should have:

• Python 3.9+ experience
• Understanding of basic ML concepts
• Familiarity with APIs
• OpenAI API key (or alternative LLM API)

Related Learning Paths

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Ready to start? Begin with Building Your First RAG System and work your way through the learning path.

Have questions? Open a discussion or check the contribution guide.