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
┌─────────────────────────────────────────────────────────────────────────────┐
│ RAG LEARNING PATH │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ BASIC │ │
│ │ Document Q&A │ │
│ └──────────────────────────────────┬──────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ INTERMEDIATE │ │
│ │ │ │
│ │ Multi-Doc ──► Hybrid Search ──► Reranking ──► Conversational │ │
│ │ │ │ │
│ │ Self-RAG ◄── HyDE RAG ◄── Adaptive RAG ◄── Corrective RAG ◄────── ┘ │
│ └──────┬──────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ ADVANCED │ │
│ │ │ │
│ │ Production ──► Graph RAG ──► Multi-Modal ──► Agentic │ │
│ │ │ │ │
│ │ Long Context ◄── Docling ◄── Speculative ◄── Modular ◄─────────── ┘ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘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 |
| Self-RAG | Self-correcting RAG with query rewriting and answer verification | ~5 hours |
| HyDE RAG | Hypothetical document embeddings for better retrieval | ~4 hours |
| Adaptive RAG | Query complexity routing to optimal retrieval strategies | ~5 hours |
| Corrective RAG | Retrieval evaluation with web search fallback | ~5 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 |
| Modular RAG | Composable framework with swappable components | ~4 days |
| Speculative RAG | Parallel draft generation with verification for speed and accuracy | ~4 days |
| Document RAG with Docling | Advanced document parsing with table extraction, OCR, and multi-format support | ~4 days |
| Long Context RAG | Leverage 128K+ context windows for full document understanding | ~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 |
Case Studies
Real-world implementations showing RAG in production environments.
| Case Study | Industry | Description | Status |
|---|---|---|---|
| Enterprise Customer Support | SaaS | 100K+ ticket handling with intelligent routing and auto-response | Available |
| Legal Contract Analysis | Legal Tech | Contract review, clause extraction, and risk identification | Available |
| Medical Literature Search | Healthcare | Medical research papers, clinical trials, and drug interactions | Available |
| Financial Research Assistant | Finance | SEC filings, earnings calls, and investment analysis | Available |
Case studies demonstrate complete production systems with architecture, code, deployment, and business metrics.
Key Concepts
┌─────────────────────────────────────────────────────────────────────────────┐
│ RAG KEY CONCEPTS │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────┐ │
│ │ RAG │ │
│ └────┬────┘ │
│ ┌───────────────────────┼───────────────────────┐ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌────────────┐ ┌────────────┐ ┌────────────┐ │
│ │ Retrieval │ │Augmentation│ │ Generation │ │
│ └─────┬──────┘ └─────┬──────┘ └─────┬──────┘ │
│ │ │ │ │
│ • Embeddings • Chunking • LLM Selection │
│ • Vector Search • Context Window • Temperature │
│ • Reranking • Prompt Engineering • Streaming │
│ • Hybrid Search │
│ │
└─────────────────────────────────────────────────────────────────────────────┘Frequently Asked Questions
What is RAG and why should I use it?
RAG (Retrieval-Augmented Generation) is a technique that combines information retrieval with LLM generation. Instead of relying solely on the LLM's training data, RAG retrieves relevant documents from your knowledge base and uses them as context for generating responses. This reduces hallucinations, enables up-to-date responses, and allows LLMs to work with your private data.
What's the difference between RAG and fine-tuning?
Fine-tuning modifies the model's weights to learn new information, requiring significant compute and retraining when data changes. RAG keeps the model unchanged and retrieves relevant context at query time, making it easier to update knowledge, more cost-effective, and better for factual accuracy. Most production systems use RAG for knowledge-grounded responses and fine-tuning for style/behavior changes.
Which vector database should I use for RAG?
For learning and prototypes, use ChromaDB (simple, local, free). For production with <1M vectors, consider Pinecone (managed, easy), Weaviate (hybrid search), or Qdrant (performance). For billion-scale search, use FAISS with IVF/HNSW indexes or Milvus. The choice depends on scale, budget, and whether you need managed vs self-hosted.
How do I improve RAG accuracy?
Key techniques include: (1) Better chunking strategies with semantic boundaries, (2) Hybrid search combining keyword + vector retrieval, (3) Reranking with cross-encoders, (4) Query rewriting and expansion, (5) Iterative retrieval with self-correction. Our intermediate and advanced projects cover all these techniques.
What's the typical RAG architecture?
A standard RAG pipeline has: Document ingestion (load, chunk, embed, store) → Query processing (embed query, retrieve documents) → Generation (construct prompt with context, generate response). Production systems add caching, evaluation, monitoring, and fallback mechanisms.
How much does it cost to run a RAG system?
Costs depend on scale. For a small system: Embedding generation ~$0.0001/1K tokens with OpenAI, vector storage ~$25-70/month for 1M vectors on managed services (free with local ChromaDB), LLM generation ~$0.01-0.03 per query with GPT-4o-mini. Semantic caching can reduce costs by 40-60%.
Start with the Intelligent Document Q&A project to learn the fundamentals.