RAG (Retrieval Augmented Generation)

What is RAG?

Retrieval Augmented Generation (RAG) is an AI architecture pattern that combines information retrieval with text generation. Instead of relying solely on what the AI learned during training, RAG first searches a knowledge base for relevant documents, then uses that information to generate an accurate, grounded response.

How Does RAG Work?

1. Query Processing: The user asks a question or gives a task
2. Retrieval: The system searches a vector database or document store for relevant information
3. Context Assembly: Retrieved documents are combined with the original query
4. Generation: The AI generates a response grounded in the retrieved information
5. Citation: The response includes references to source documents

RAG vs Fine-Tuning

Aspect	RAG	Fine-Tuning
Data freshness	Always current	Frozen at training time
Cost	Low (no retraining)	High (GPU hours)
Transparency	Sources are traceable	Black box
Setup	Index documents	Prepare training data
Best for	Factual Q&A, knowledge bases	Style/behavior changes

RAG Use Cases

• Customer support: Answer questions from product documentation
• Legal research: Find relevant cases and precedents
• Internal knowledge: Search company wikis and SOPs
• Content creation: Ground blog posts in research and data

Key Takeaway: RAG is the most practical way to make AI accurate and trustworthy — it grounds responses in real data instead of guessing.