RAG Pipeline

Overview

RAG grounds an LLM in your data by retrieving relevant context at query time. Most RAG quality problems are retrieval problems, not generation problems — if the right chunk isn't retrieved, no prompt can save the answer. Optimize retrieval first.

When to use

• The model must answer over private/large/changing documents.
• You need citations and reduced hallucination.
• Fine-tuning is overkill or data changes too often.

Pipeline stages