Understanding RAG: A Beginner’s Guide to Retrieval Augmented Generation

Before we understand what RAG is, let’s first talk about why RAG exists in the first place.
What’s the purpose of its existence? Why should we learn it? How does it make LLM more powerful and useful?

Imagine you're asking a large language model (LLM) like Chatgpt a question about a very recent cricket match, or maybe something inside a private PDF you uploaded. Chances are, it might not know the answer. But why? We often assume that LLM or AI know everything on the internet. So why can’t it answer your question?

Well, this happens because of something called the knowledge cutoff.

What is a Knowledge Cutoff?

A knowledge cutoff is the date up to which a large language model (like Chatgpt or Gemini) was trained on information. Means, the model knows things that happened before the cutoff date, but it doesn’t know anything that happened after that date.

Example: Let’s say the knowledge cutoff is June 2024.

• The model knows about Chatgpt plugins, GPT-4 launch, or events that happened before June 2024.

• But it won’t know about the 2025 election results, the latest iPhone, or your newest blog, unless you tell it!

Why is this important?

When you ask an LLM something like, Who won the IPL in 2025? If the model’s knowledge cutoff is June 2024, it might either say I don’t know or make a guess based on patterns or older data, which could be wrong and when a model makes a guess based on patterns or incomplete knowledge and gives an answer that sounds correct but is wrong, it’s called a hallucination.

What Is a Hallucination?

A hallucination happens when the AI confidently says something that isn’t true, or that isn’t based on real data. It’s not lying, it’s just guessing based on patterns llms had learned. But because it sounds so confident, it can be misleading.

So far, we have seen that there is a problem that LLMS do not have real-time access to the internet, your private files, or any custom database.

That means they can’t answer questions about recent events, private company documents, or custom knowledge unless you provide that information manually.

Retrieval Augmented Generation ( RAG 🫡)

Now we know the problem, and for this problem, RAG is a technique that solves this problem. It allows the large language model (LLM) to retrieve relevant information from external sources and use that information to generate better, more accurate answers.

Think of RAG like giving your LLM a research buddy or assistant. Before the LLM answers the user's question, this buddy says: Hold on, let me quickly check the right document or source before we reply

Instead of guessing like before, the LLM now has real info to back up its answer.
So it's not just smart, it is informed. RAG turns your LLM into a smart assistant with internet and file access. It doesn’t just think, it checks first

In short, RAG:

1. Fetch information (Retrieval)

2. Add it to the model’s knowledge (Augmentation)

3. Generate the answer (Generation)

RAG is made up of three words: Retrieval, Augmented, and Generation. Let us understand them one by one.

Retrieval

This is the first step. Before the model answers your question, it searches or fetches relevant information from a trusted source. This source could be: A set of documents (PDF, text files), A private database, A knowledge base, or even the whole internet (if allowed)

Augmented

Now that the LLM has the relevant documents or information from the data source, it augments itself or becomes aware, which means it adds this information to its current understanding or context. LLMS (Large Language Models) are trained on lots of data, and now we have given some extra context, which makes the model smarter, more context-aware, and more accurate.

Generation

Finally, the language model uses the information it retrieved + its language skills (Natural Language Processing) to generate a response. This is the step where you get a nice, fluent, human-like answer but now it has real data, not just guesses!

Before RAG vs After RAG

Conclusion

Simple, right? We have explored how RAG works to give us real-time, structured responses by leveraging both our data sources and the LLM's knowledge. Large Language Models have powerful natural language processing capabilities, which help them respond in a natural, human-like way. While RAG implementation can be technically complex, don't worry, I'll be here to explain and simplify those concepts for you in future posts.

If you understood this explanation of RAG, please leave a comment below. Your feedback is incredibly valuable, as this is my first blog, and it helps me understand if I'm explaining these concepts clearly or not. Don't forget to like this article if you found it helpful! I'll be back soon with another easy-to-understand explanation article about AI concepts.

Let me know your thoughts in the comments!