Retrieval-Augmented Generation Architecture
Retrieval-Augmented Generation is the architectural pattern that allows Large Language Models to reason over real, verifiable, and continuously updated data sources. By combining retrieval and generation, RAG systems reduce hallucinations and enable AI applications to operate with contextual awareness and factual grounding.
What problem RAG solves
Retrieval-Augmented Generation combines information retrieval with generation, grounding model responses in trusted sources.
- Reduces hallucinations by grounding responses in real data
- Enables models to reason over private and enterprise knowledge
- Decouples model capabilities from knowledge freshness
- Improves transparency and traceability of generated answers
Core components of a RAG system
A typical RAG architecture includes document ingestion, embedding, vector search, contextual retrieval, and controlled generation.
RAG architecture at a glance
A typical RAG pipeline includes document ingestion and chunking, embedding generation, vector-based retrieval, contextual prompt assembly, and controlled generation using retrieved evidence as grounding context.
Using Arcana to explore RAG patterns
Arcana lets you interrogate RAG architectures using curated knowledge rather than probabilistic guesses.
Applied use case
See how Retrieval-Augmented Generation is applied in real enterprise environments to enable semantic document search.
Enterprise document search with RAG architectures