RAG Systems
RAG Projects
Master Retrieval-Augmented Generation from basics to production
RAG Projects
Retrieval-Augmented Generation combines the power of search with LLM generation for accurate, grounded responses. RAG is one of the most practical AI patterns in production today.
Learning Path
Projects
Beginner
| Project | Description | Time |
|---|---|---|
| Intelligent Document Q&A | Build a complete RAG system for PDF documents | ~2 hours |
Intermediate
| Project | Description | Time |
|---|---|---|
| Multi-Document RAG | Handle multiple documents with context management | ~4 hours |
| RAG with Reranking | Improve retrieval accuracy with reranking | ~4 hours |
| Hybrid Search | Combine keyword and semantic search | ~4 hours |
| Conversational RAG | Add memory and context to your RAG system | ~4 hours |
Advanced
| Project | Description | Time |
|---|---|---|
| Production RAG Pipeline | End-to-end production system with evaluation, monitoring, caching | ~3 days |
| Graph RAG | Knowledge graph enhanced retrieval with Neo4j | ~3 days |
| Multi-Modal RAG | Handle images, tables, and complex PDFs | ~4 days |
| Agentic RAG | Self-correcting RAG with autonomous agents | ~4 days |
Why Learn RAG?
| Benefit | Description |
|---|---|
| Accuracy | Grounds LLM responses in your data |
| Control | Limits hallucinations with source attribution |
| Scalability | Works with any document corpus size |
| Privacy | Keep your data in your infrastructure |
Key Concepts
Start with the Intelligent Document Q&A project to learn the fundamentals.