What is a RAG pipeline?

A RAG pipeline combines search with AI generation. When you ask a question, it first searches a knowledge base for relevant documents, then feeds those documents to a language model that generates an answer based on the retrieved information.

What are the main benefits of using RAG over standard AI models?

RAG systems dramatically reduce hallucinations by grounding answers in real documents, provide citable responses for verification, and enable domain expertise by connecting to your proprietary knowledge.

What are the biggest challenges in building a RAG system?

The main challenges include ensuring high-quality retrieval that finds the right documents, determining optimal chunking strategies, managing latency across pipeline steps, and maintaining knowledge base quality over time.

How do I know if my organization needs a RAG system?

Consider RAG if employees waste time searching for information, you need AI grounded in current or proprietary data, you're in a regulated industry requiring verifiable responses, or accuracy and domain expertise matter more than creative generation.

Can RAG work with different types of content beyond text?

Yes, modern RAG systems handle multimodal content including images (using OCR or vision models), tables (converted to searchable formats), diagrams (with textual descriptions), and code (with syntax-aware processing).