Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique that grounds AI responses in verified, domain-specific information by retrieving relevant content from a knowledge base before generating an answer. It's the most effective way to make AI agents accurate and knowledgeable without expensive model training.

The RAG pipeline: user sends a question, the question is converted to an embedding vector, similar vectors are searched in the knowledge base, the most relevant content chunks are retrieved, these chunks are added to the AI's context alongside the question, and the AI generates a response grounded in the retrieved information.

RAG solves key AI limitations: reduces hallucination (responses grounded in real data), provides current information (knowledge base can be updated instantly), enables domain expertise (AI becomes an expert in your specific content), maintains source attribution (can cite where information came from), and is cost-effective (no model training needed — just upload content).

RAG quality depends on: content quality (well-written, comprehensive source material), chunking strategy (how documents are split into retrievable pieces), embedding model (how well semantic meaning is captured), retrieval algorithm (how relevant chunks are selected), and prompt design (how retrieved content is presented to the AI).

Advanced RAG techniques include: hybrid search (combining keyword and semantic search), re-ranking (using a second model to re-order retrieved results), query expansion (reformulating the query for better retrieval), contextual compression (summarizing retrieved chunks to fit context windows), and multi-hop retrieval (retrieving information in multiple steps for complex questions).

On Chipp, RAG powers the knowledge base feature. Builders upload documents, websites, audio, and video — the platform handles chunking, embedding, and retrieval automatically, making their agents domain experts.