From RAG Concepts to a Real Project: A Beginner’s Guide
RAG can sound complicated when you first encounter embeddings, vector databases, retrieval, and LLMs. Here’s a simple, practical explanation of how RAG works and how beginners can approach building their first RAG application.
From RAG Concepts to a Real Project: A Beginner’s Guide
When I first started learning about Generative AI, I kept coming across the term RAG (Retrieval-Augmented Generation).
At first, it sounded more complicated than it actually is.
Words like embeddings, vector databases, retrieval, context, and LLMs can make RAG feel intimidating when you are just getting started.
So in this article, I want to explain RAG in a simple way and show how the different pieces fit together.
What is RAG?
RAG stands for Retrieval-Augmented Generation.
The basic idea is simple:
Instead of asking an LLM to answer a question only from what it already knows, we first retrieve relevant information and give that information to the LLM as context.
A simple RAG workflow looks like this:
User Question → Retrieve Relevant Information → Add Context → LLM → Final Answer
This makes it possible to build applications that can answer questions using your own documents or knowledge base.
Why do we need RAG?
Large Language Models are powerful, but they don't automatically know the private or specific information that you want your application to use.
For example, imagine you have:
- College notes
- Company documentation
- Research papers
- Product manuals
- Legal documents
- Internal knowledge bases
You could try to put everything into a prompt, but that isn't practical for large collections of documents.
RAG provides another approach.
Instead of sending all documents to the model, the system searches for the most relevant information and sends only that context to the LLM.
How does a RAG system work?
A basic RAG system usually has several important steps.
1. Collect your documents
First, we need a knowledge source.
This could be PDFs, websites, text files, documentation, or databases.
For example:
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documents/
├── aws_basics.pdf
├── cloud_computing.pdf
├── genai_notes.pdf
└── project_documentation.pdfEnjoyed reading this content? Let the author know!
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