What Is RAG? Retrieval-Augmented Generation Explained

Understand how RAG works and why it makes AI responses more accurate and grounded in facts.

In this guide (5 steps):

1.The problem RAG solves
2.How RAG works
3.Embeddings in plain language
4.Vector databases
5.When to use RAG

The problem RAG solves

~15s

LLMs have a knowledge cutoff and can hallucinate. RAG lets AI search your own documents first, then answer based on actual data.

How RAG works

~15s

Step 1: Convert your docs into embeddings (mathematical representations). Step 2: When asked a question, find relevant chunks. Step 3: Send those chunks + the question to the LLM.

Embeddings in plain language

~15s

Embeddings turn text into numbers that capture meaning. "Happy" and "joyful" have similar numbers, so AI knows they're related.

Vector databases

~15s

Pinecone, Weaviate, and Chroma store embeddings for fast retrieval. They're like search engines for meaning, not just keywords.

When to use RAG

~15s

Customer support bots, internal knowledge bases, research assistants, and any application where accuracy matters more than creativity.

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