Imagine asking ChatGPT:
“What is our company's leave policy?”
It can be a great question, but unless that information is in its knowledge, the model can't magically know your company's internal rules.
This is where RAG comes in.
So, what is RAG?
RAG = Retrieval-Augmented Generation
In simple terms:
Search first. Answer second.
Instead of asking the LLM to answer from what it already knows, we first retrieve relevant information from our own data and give that context to the model.
User Question
↓
Search your data
↓
Find relevant information
↓
Send it to the LLM
↓
Generate the answer
A simple example
Suppose you have 500 company documents.
A user asks:
“How many days of annual leave do I get?”
RAG searches those documents and finds:
“Employees receive 20 days of annual leave per year.”
That information is then passed to the LLM.
The LLM turns it into a natural response:
“You get 20 days of annual leave per year.”
The LLM didn't need to memorize your employee handbook.
It just needed to find the right piece of information.
What happens behind the scenes?
Before users start asking questions, your documents usually go through a preparation process:
Documents
↓
Split into smaller chunks
↓
Convert chunks into embeddings
↓
Store embeddings in a vector database
When a user asks something:
Question
↓
Convert question into an embedding
↓
Search for similar chunks
↓
Retrieve relevant content
↓
LLM generates the answer
This is the basic RAG pipeline.
Where can you use RAG?
Pretty much anywhere an AI needs access to external or private knowledge:
📄 Document Q&A
Ask questions about PDFs, reports, contracts, or research papers.
📚 Documentation assistants
Build an AI that understands your product or API documentation.
🏢 Internal knowledge
Search company policies, processes, and internal documents using natural language.
💬 Customer support
Retrieve relevant product information before generating a response.
RAG vs Fine-tuning
A simple way to remember the difference:
RAG → Give the model knowledge
Fine-tuning → Change the model's behavior
If your company's documentation changes frequently, RAG can be useful because you can update the knowledge base without retraining the model.
One important thing 🚨
RAG doesn't magically eliminate hallucinations.
If the system retrieves the wrong information, the LLM can still produce the wrong answer.
So a good RAG system isn't just:
LLM + Vector Database
It also involves good:
- Chunking
- Embeddings
- Retrieval
- Ranking
- Prompting
- Evaluation
And that's where things start getting interesting.
RAG is basically giving an LLM a searchable external memory.
In the next part, we'll break down what actually happens inside that pipeline, from chunking → embeddings → vector search → retrieval → final answer.
