Getting Started with RAG: Building Your First Retrieval Augmented Generation System
Learn the fundamentals of RAG systems, from vector databases to prompt construction, and build your first working implementation. This comprehensive guide covers everything from basic concepts to production-ready patterns.
Getting Started with RAG: Building Your First Retrieval Augmented Generation System
Retrieval Augmented Generation (RAG) is one of the most practical and impactful applications of large language models today. By combining the knowledge of LLMs with your organization's specific data, RAG systems enable AI assistants that understand your business, products, and processes.
What is RAG?
RAG enhances LLMs by retrieving relevant information from your knowledge base before generating responses. This solves two critical problems:
- Hallucination: LLMs trained on public data don't know your proprietary information
- Accuracy: By grounding responses in retrieved documents, you get more accurate, verifiable answers
The RAG pipeline consists of three main stages: document ingestion, vector storage, and retrieval & generation.