If you've used ChatGPT or Claude, you've noticed they sometimes state wrong "facts" confidently. This is called hallucination—and RAG is the engineering solution.
How Does RAG Work?
RAG operates in three phases. Originally introduced by Meta AI in 2020, it's now the standard for production AI.
Phase 1: Document Processing
Documents get broken into chunks (200-500 tokens) and converted to embeddings—numerical vectors stored in a vector database like Pinecone or Weaviate.
Phase 2: Retrieval
When you ask a question, it becomes a vector too. The system finds the most relevant document chunks via similarity search.
Phase 3: Augmented Generation
Retrieved passages get injected into the prompt. The model generates responses grounded in actual sources, not just training data.
Why RAG Over Fine-Tuning?
Instant updates. Swap documents, and the AI knows immediately. No retraining needed.
Source attribution. RAG can cite exactly which documents informed a response.
Cost effective. 3-10x cheaper than fine-tuning large models.
Common Mistakes
Chunk size matters. Too small loses context; too large dilutes relevance.
Hybrid search wins. Combine vectors with BM25 keyword matching for best results.
Try our Knowledge Base tool to experiment with RAG concepts in your browser.