RAG Explained: How AI Answers From Your Data

1Collect your documents and split them into chunks.

This section provides key insights and practical guidance.

2Create embeddings and store them in a vector database.

This section provides key insights and practical guidance.

3On a question, retrieve the most relevant chunks.

This section provides key insights and practical guidance.

4ANSWER the model replies using that context (and can cite it)

This section provides key insights and practical guidance.

5How Retrieval Works

The retrieval step usually uses embeddings. Your documents are converted into vectors (numbers that

capture meaning) and stored in a vector database. When a question comes in, it's also turned into a

6Why RAG Helps

This section provides key insights and practical guidance.

• Accuracy — answers are grounded in real documents, reducing hallucinations.
• Freshness — update the documents and answers update too, no retraining.
• Sources — the model can cite where its answer came from.
• Privacy — it works with your own data without baking it into the model.

7RAG vs Fine-Tuning

People often confuse the two. RAG looks information up at answer time — ideal for facts and documents

that change. Fine-tuning bakes knowledge or style into the model itself — better for changing behaviour

• Chatbots that answer from a company's help docs or policies.
• Search assistants over internal knowledge bases.
• Tools that answer questions about long PDFs or contracts.
• Customer support grounded in product documentation.
• Garbage in, garbage out — answers are only as good as the documents.