Why the Need for RAG (Retrieval-Augmented Generation)?

Picture this: You're chatting with an AI assistant, and you ask it about your company's latest quarterly report. The AI responds with generic business advice because it has zero clue about your specific documents. Frustrating, right?

The problem is LLMs don’t have access to real-time information until they are trained up to the current time. Suppose the techies in the company think about training the model regularly, but it is quite hectic and not cost-effective. Somehow, LLMs answer queries based on pre-trained data—generic responses. But this is of no use to the company.

What if the LLMs could answer based on the company's database? I guess that would solve the problem; it's like hiring an expert to come up with a solution for a specific problem or needing someone to provide insights.

That's how AI engineers came up with a solution—RAG (Retrieval-Augmented Generation) was born. Pretty cool, right?.

The Courtroom Analogy: How RAG Works

Let’s make this simple — imagine you're in a courtroom.

• The judge is smart, experienced, and speaks eloquently.

• But even judges don’t remember every law or past case off the top of their head.

• So, when a complex case comes up, they call for their clerks.

The judge says:

“Bring me the laws, rulings, or past case files related to this situation.”

The clerks head to the massive legal library 📚, search for the most relevant documents, and return with useful files.

The judge reads the documents, uses their expertise to interpret them, and gives a well-informed verdict.This is how exactly RAG works.

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Now map that to RAG:

• The Judge = LLM (Large Language Model)
  It’s capable of reasoning and generating fluent answers — but it needs specific context.

• The Clerks = Retriever
  This part of the system fetches relevant documents from a custom data source — company docs, PDFs, reports, webpages, etc.

• The Legal Library = Knowledge Base / Vector Store
  It’s where all your important documents are stored in a searchable format.

• The Case Files = Retrieved Documents
  The Retriever pulls the top-k relevant files based on the user’s question.

• The Final Verdict = Generated Answer (with Context)
  The LLM reads the retrieved documents and crafts a response that is grounded, specific, and informed.

How RAG Works?

You might be wondering —
"Why not just upload raw PDFs directly into an LLM like ChatGPT and start asking questions?"

Well, that sounds ideal, but there’s a catch.

The Problem: Token Limit & Context Window

Large Language Models like GPT-4 have a token limit, known as the context window — which is the maximum amount of information the model can “remember” at once.

• GPT-3.5: ~4K tokens

• GPT-4: ~8K to 128K tokens (depending on the version)

• And still, large PDFs often exceed that.

A typical PDF report (50–100 pages) = 50,000+ tokens.

So, if you try to load everything into memory at once:

• It won’t fit,

• Or it’ll be expensive and inefficient,

• And the model might miss critical details by truncating input.

The Solution: Retrieval-Augmented Generation (RAG)

Instead of shoving the entire PDF into the model, RAG breaks the process into two smart parts:

1. Memory-efficient storage:
   Store the documents externally, in a structured, searchable format (like a vector database).

2. Precision retrieval:
   When the user asks a question, only the most relevant chunks of information are retrieved and sent to the LLM.

This keeps the input within token limits and makes responses faster, cheaper, and more accurate.

Technical Data Flow of RAG

Let’s walk through the step-by-step pipeline:

1. Document Ingestion

• Upload raw files (PDFs, Word docs, etc.).

• Use tools like PyMuPDF, pdfplumber, or LangChain to extract text.

• The extracted text is split into smaller chunks (e.g., 200–500 words).

• Each chunk is embedded into a vector using models like OpenAI Embeddings, BGE, or SBERT.

2. Vector Storage

• All embeddings are stored in a vector database (like FAISS, Chroma, Pinecone, or Weaviate) along with metadata (e.g., page number, section title, source document).

3. User Query

• User inputs a question or task.

4. Query Embedding & Retrieval

• The query is converted into a query vector.

• The vector store performs semantic search to find the most relevant document chunks.

• Top k matching chunks are retrieved.

5. Context Assembly

• The retrieved content is combined with the query and formatted as a prompt.

6. LLM Response

• The LLM reads the retrieved context + question and generates a grounded, accurate answer.

[Raw PDFs] → [Extract Text] → [Chunk & Embed] → [Store in Vector DB]
                                ↑
        [User Query] → [Embed Query] → [Retrieve Relevant Chunks]
                                ↓
      [Query + Retrieved Context] → [LLM → Generate Answer]

Generated by ChatGPT haha.

Under the Hood of RAG: Chunking, Indexing & Similarity Search

1. Why is Chunking Needed?

LLMs can’t handle very large documents all at once due to token limits, so instead of passing in the entire content, we break it into smaller, manageable pieces — this process is called chunking.

Chunking ensures that:

• Text can be indexed efficiently.

• Only the relevant pieces are retrieved during a query.

• The system stays within token limits.

Imagine reading a whole book vs. flipping to the exact chapter you need — that’s chunking in action.

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2. Choosing the Right Chunk Size

Choosing an ideal chunk size is more art than science — but here are some standard practices:

Too small = not enough context
Too large = may exceed token limits or contain unrelated content

The key is to experiment and tune based on your documents.

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3. Chunk Overlap — Keeping Context Intact

A big problem in naive chunking is context loss between chunks. For example, the first sentence in a chunk might reference the last sentence of the previous one.

To solve this, we use overlapping chunks:

• Let’s say:

  • Chunk size = 500 tokens

  • Overlap = 100 tokens

So each new chunk starts 100 tokens before the previous chunk ends.
This ensures continuity and preserves important context between chunks.

Think of it like watching a TV show — you rewatch the last 10 seconds of the previous episode so you don’t lose the thread of the story.

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4. Similarity Search — Finding the Right Chunk(s)

Once all chunks are stored in a vector database, RAG uses semantic similarity search to find the most relevant ones based on the query.

• The user’s query is embedded into a vector.

• That vector is compared against all chunk vectors using cosine similarity or dot product.

• The top-k most similar chunks are retrieved and fed into the LLM.

This is how RAG ensures contextual accuracy without needing to retrain the model.

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5. Ingestion & Indexing

Before any of this can work, your data must go through an ingestion pipeline:

Ingestion involves:

1. Extracting text from sources (PDFs, docs, websites, etc.)

2. Cleaning and preprocessing the text

3. Chunking and overlapping the text

4. Embedding each chunk using a model like OpenAI, BGE, or SBERT

5. Storing in a vector database (like FAISS, Chroma, Pinecone, etc.)

This process is usually automated and forms the foundation of your RAG system.

RAG Applications You Might Already Be Using

You’ve probably used RAG without realizing it. Here are a few everyday examples:

• Chat with PDFs: Tools like ChatGPT (with file upload), ChatPDF, and Humata.ai let you ask questions on documents — powered by RAG.

• Company Chatbots: Internal assistants that answer HR, policy, or support queries use RAG to pull info from internal databases.

• AI Search Engines: Tools like Perplexity.ai and You.com retrieve real-time info and generate summaries using RAG.

• Coding Assistants: GitHub Copilot and similar tools use RAG to fetch relevant code examples before generating suggestions.

RAG is everywhere — helping apps give smarter, more grounded answers using your data

Wrapping Up!!

Retrieval-Augmented Generation (RAG) bridges the gap between generic language models and specific, real-world knowledge.

Instead of retraining models every time your data changes, RAG gives your AI the ability to search, understand, and respond using the most relevant information — just like a well-prepared expert.

Whether you're building document assistants, enterprise chatbots, or AI search tools, RAG offers a scalable, cost-effective, and smarter way to use LLMs in the real world.

In short: Train less, retrieve more, answer smarter.

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Still confused if you should use RAG?
Well… don’t worry — even LLMs retrieve their courage before generating a response.

Keep reading — we’re just getting started!