Retrieval Augmented Generation (RAG): A Beginner's Guide
Generative AI has taken the world by storm. Tools like ChatGPT can write essays, answer questions, and even generate code. But sometimes, these models "hallucinate"—they make up facts or give outdated answers because they only rely on the data they were trained on.
This is where Retrieval Augmented Generation (RAG) comes in. RAG combines the power of search with the creativity of AI generation, making AI responses more accurate, up-to-date, and trustworthy.
What is RAG?
Retrieval Augmented Generation (RAG) is a technique where an AI model doesn't just generate answers from its training. Instead, it first retrieves relevant information from an external knowledge base (like documents, PDFs, or a database) and then uses a generator (a large language model) to create a natural, human-like answer.
Think of it like this:
- Without RAG → Asking a friend to answer only from memory
- With RAG → Asking a friend to check their notes or Google first, and then give you the answer
Why is RAG Used?
RAG is used to:
- Reduce hallucinations (wrong answers)
- Add up-to-date knowledge that wasn't in the model's training
- Customize responses with domain-specific data (e.g., company documents, medical guidelines)
- Improve trust and transparency, since answers can be tied to sources
How RAG Works: Retriever + Generator
RAG has two main parts:
- Retriever – Finds relevant information from a knowledge base
- Generator – Uses an LLM to create a fluent answer based on the retrieved info
Simple Example
Suppose you ask:
What is the capital of France, and give me a fun fact about it?
Retriever: Looks into a knowledge base, finds documents mentioning "France → Capital → Paris" and "Paris facts → Eiffel Tower, culture."
Generator: Reads those snippets and responds:
The capital of France is Paris. A fun fact is that the Eiffel Tower was originally meant to be a temporary structure!
Here, the retriever ensures the model has the right facts, and the generator makes the response smooth and human-like.
What is Indexing?
To search documents quickly, we need an index—like the index in a book.
Instead of flipping through every page, the index lets us jump straight to the relevant section.
In RAG, indexing means organizing documents in a way that the retriever can quickly find the most relevant pieces of text.
Why do we perform Vectorization?
Computers don't understand text the way humans do. They need numbers.
Vectorization means converting text into number-based representations called embeddings.
- Similar meanings → similar vectors
- Different meanings → far apart vectors
Example
- "Paris" and "capital of France" will have vectors close to each other
- "Banana" and "capital of France" will be far apart
This helps the retriever know what information is related to your question.
Why do RAGs Exist?
RAGs exist because LLMs can't know everything:
- Training data gets outdated
- Models have size limits—they can't memorize all possible knowledge
- Different users need different knowledge (a doctor vs. a lawyer vs. a student)
RAG solves this by connecting the model to fresh, specific, and external knowledge sources.
Why do we perform Chunking?
Most documents are long. If we dump an entire 200-page PDF into the model, it won't work well.
Chunking means breaking documents into smaller pieces (e.g., 500–1000 characters each).
- Smaller chunks → easier to store, search, and retrieve
Example
A 10-page company policy is split into smaller paragraphs so the retriever can pull out only the relevant section.
Why is Overlapping used in Chunking?
Sometimes, important context sits on the boundary of two chunks.
- Without overlap: We might lose meaning
- With overlapping: Each chunk shares a little content with its neighbor
Example
Chunk 1: "...employees must log in daily. Passwords should be..."
Chunk 2 (overlap): "...Passwords should be at least 12 characters long..."
This way, the retriever doesn't miss context when searching.
Final Thoughts
Retrieval Augmented Generation (RAG) is like giving AI a library card—it can look up the right information before answering. By combining retrieval (search) and generation (language models), RAG helps build AI systems that are:
- More accurate
- More reliable
- More useful for real-world applications
As GenAI keeps evolving, RAG will be one of the core techniques powering enterprise chatbots, research assistants, and intelligent knowledge systems.