> ## Documentation Index > Fetch the complete documentation index at: https://praison.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # RAG Overview > Retrieval Augmented Generation with PraisonAI # RAG Overview **Retrieval Augmented Generation (RAG)** combines the power of large language models with your own data. Instead of relying solely on the model's training data, RAG retrieves relevant information from your documents and uses it to generate accurate, grounded answers. ## How It Works ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} flowchart LR Q[Question] --> R[Retrieve] R --> C[Context] C --> G[Generate] G --> A[Answer + Citations] ``` 1. **Index**: Your documents are chunked and stored as embeddings 2. **Retrieve**: When you ask a question, relevant chunks are found 3. **Generate**: The LLM uses retrieved context to answer 4. **Cite**: Sources are tracked for transparency ## Architecture PraisonAI's RAG is built on a simple principle: > **Knowledge indexes; RAG answers with citations.** * **Knowledge**: Handles document ingestion, chunking, embedding, and retrieval * **RAG**: Thin orchestrator that combines Knowledge retrieval with LLM generation This separation keeps the core SDK lean while providing powerful RAG capabilities. ## Quick Example ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent # Simplest approach - just works agent = Agent( name="Research Assistant", knowledge=["research_paper.pdf", "data/"], ) response = agent.start("What are the key findings?") print(response) ``` ## When to Use RAG | Use Case | RAG Helps? | | --------------------------- | --------------------- | | Q\&A over documents | ✅ Yes | | Summarizing reports | ✅ Yes | | Code documentation lookup | ✅ Yes | | General knowledge questions | ❌ No (use base LLM) | | Real-time data | ❌ No (use tools/APIs) | ## Key Features Every answer includes source references Real-time response streaming Share knowledge across agents Full CLI for indexing and querying ## Next Steps Get started in 5 minutes Detailed API reference Command-line usage Working with sources