What Is RAG?

Retrieval-Augmented Generation, commonly called RAG, is an architecture that allows an AI application to answer questions using information the model was never trained on.

Instead of relying entirely on information stored inside a model's parameters, a RAG system retrieves relevant text at request time and hands it to the model as context before generation.

Why RAG matters

A language model may not know about:

• Your company's internal documentation
• Your database schema
• Private business data
• Recently created documents
• Product-specific information

RAG provides a way to connect an AI application to this external information without retraining the model.

The basic architecture

A typical RAG system looks like this:

User
  ↓
Question
  ↓
Embedding
  ↓
Vector Search
  ↓
Relevant Documents
  ↓
Prompt + Context
  ↓
LLM
  ↓
Answer

The important idea is that retrieval happens before generation.

RAG is more than a vector database

A production RAG system usually involves several components:

1. Document ingestion
2. Text extraction
3. Chunking
4. Embeddings
5. Vector storage
6. Retrieval
7. Context construction
8. Prompting
9. Generation
10. Evaluation

A vector database is only one part of the system — the retrieval and context-construction steps around it matter just as much for answer quality.

What we will build

Throughout AI Engineering Foundry, we'll build RAG systems using:

The focus is practical implementation — what broke, what we validated, and why — rather than theory alone.

What's next

The next article in this discipline builds a working RAG pipeline end to end with ASP.NET Core and Qdrant.