RAG App Stack

A Retrieval-Augmented Generation (RAG) stack is a software architecture that combines language models with external knowledge retrieval systems to generate responses using information beyond a model's built-in knowledge. These architectures support AI search systems, enterprise knowledge assistants, document intelligence platforms, research tools, operational AI applications, and conversational systems that require accurate, up-to-date information.

Typical Architecture

A common RAG architecture looks like this:

User Query
      ↓
Frontend Interface
      ↓
Retrieval Pipeline
      ↓
Knowledge Retrieval
      ↓
Language Model
      ↓
Response with Citations

Simple Architecture

A minimal RAG stack may include:

Document Collection
Embedding Generation
Knowledge Index
Language Model
Chat Interface

Production Architecture

A larger production deployment may include:

Frontend AI Workspace
Document Processing Pipelines
Embedding Generation
Knowledge Indexes
Hybrid Retrieval
Reranking Models
Language Model Routing
Citation Generation
Realtime Indexing
Permission Management
Workflow Automation
Monitoring Infrastructure
Analytics Pipelines
Knowledge Graphs
Evaluation Systems