The definitive interactive guide to building intelligent AI systems that retrieve, reason, and generate with real-world knowledge.
Retrieval-Augmented Generation is a technique that enhances Large Language Models by fetching relevant information from external knowledge bases before generating a response — grounding outputs in real, verifiable data.
RAG connects LLMs to external knowledge bases, ensuring responses are based on actual data rather than parametric memory alone.
Unlike fine-tuning, RAG systems can access the latest information by updating the knowledge base without retraining the model.
Keep sensitive data in your own vector database. The LLM never stores your proprietary information in its weights.
Click each step to explore the RAG pipeline in detail.
How you split your documents dramatically impacts retrieval quality. Try different strategies below.
Embeddings transform text into numerical vectors that capture semantic meaning. Similar concepts end up close together in vector space.
The backbone of every RAG system. These specialized databases store and search through millions of embedding vectors at lightning speed.
Fully managed vector database with serverless architecture. Zero infrastructure overhead with automatic scaling.
Open-source vector database with GraphQL API. Built-in vectorization modules and hybrid BM25 + vector search.
Lightweight, developer-friendly embedding database. Perfect for prototyping and local development with Python-native API.
High-performance distributed vector database built for scale. Cloud-managed version available as Zilliz Cloud.
Rust-built vector similarity engine with rich filtering. Excellent performance with advanced payload filtering capabilities.
PostgreSQL extension for vector similarity search. Use your existing Postgres infrastructure — no new database needed.
The generation engine. Choose the right LLM based on your latency, cost, and accuracy requirements.
200K context window. Excellent at following complex instructions with retrieved context. Strong reasoning and minimal hallucination.
128K context. Great at synthesizing information from multiple retrieved documents. Multimodal capabilities for image + text RAG.
Open-source powerhouse. Run locally for complete data privacy. Fine-tune on your domain for optimized RAG performance.
Efficient MoE architecture. Excellent cost-to-performance ratio. Great for high-throughput RAG systems on a budget.
From simple pipelines to sophisticated agentic systems — explore how RAG evolves.
Simple retrieve-then-read approach. Documents are chunked, embedded, and stored. At query time, the most similar chunks are retrieved and passed to the LLM as context.
Adds optimization stages around retrieval: query transformation, hypothetical document embeddings (HyDE), re-ranking with cross-encoders, and context compression to maximize relevance.
Modular architecture where each component (retriever, reader, router) can be swapped independently. Enables multi-source retrieval, query routing, and adaptive strategies.
An AI agent that decides when and how to retrieve, can use multiple tools, self-reflects on retrieval quality, and iteratively refines its approach. Think: AI researcher, not just a pipeline.
A structured path from beginner to RAG architect. Click each milestone to expand.
Build a strong foundation in the underlying concepts.
Understand how text becomes searchable vectors.
Build your first end-to-end RAG system.
Optimize every stage of the pipeline.
Deploy robust, scalable RAG systems.
Push the boundaries of what's possible.
Test your understanding with these interactive questions.