RAG Architecture for Production AI Systems

RAG Is a System Pattern

Retrieval-augmented generation is not just adding documents to a prompt. A production RAG system includes ingestion, parsing, chunking, embedding, indexing, retrieval, ranking, context assembly, generation, citation handling, evaluation, and monitoring.

Core RAG Components

• Document ingestion and normalization.
• Chunking strategy and metadata design.
• Embedding generation and index updates.
• Hybrid search, filters, reranking, and context selection.
• Grounded generation with citations and answer constraints.
• Evaluation for retrieval quality and answer faithfulness.

Measure Retrieval Separately

If the model receives weak context, the answer will usually be weak. Track retrieval recall, precision, stale documents, missing sources, hallucination rate, and user feedback separately from generation quality.