method#Artificial Intelligence#Machine Learning#Architecture#Data

RAG Implementation

A practical guide for implementing Retrieval-Augmented Generation (RAG). Describes architecture patterns, data flows, and evaluation criteria for knowledge-grounded generative AI systems.

Retrieval-Augmented Generation (RAG) is a method that augments generative models with external retrieval of documents to ground responses in factual knowledge.

Maturity

Emerging

Cognitive loadHigh

360° overview

RAG Implementation

Six perspectives place the building block in context. The numbers show where each perspective continues in the reading path.

The building block at a glance

RAG Implementation

360°

Classification

2

Complexity: High
Impact area: Technical

Technical context

2

Integrations

Vector databases (e.g., Milvus, FAISS, Pinecone)

+2

Principles & goals

1

Separation of retrieval and generation for better traceability.

Value stream stage: Build

Use cases & scenarios

4

Use cases: 3
Scenarios: 3

Compromises

3

Risks: 3
Best practices: 3

I/O & resources

4

Inputs: 3
Outputs: 3