We have all been there. You ask an LLM (Large Language Model) a specific question about your company's policy or a document you wrote last week, and it confidently gives you a completely wrong answer.
It’s not broken. It just doesn't know you.
ChatGPT, Claude, and Llama are trained on the public internet. They don't have access to your private Google Drive, your Notion docs, or your customer support logs.
This is where RAG (Retrieval-Augmented Generation) comes in.
It sounds fancy, but it’s actually a very simple concept. It’s the difference between taking a test from memory vs. taking an open-book test. RAG is simply giving the AI the textbook before asking it the question.
Here is how you build one from scratch, without getting lost in complex jargon.
The Architecture: Three Simple Steps
Forget the complex diagrams for a second. A RAG system does three things:
Index: It reads your data and organizes it.
Retrieve: It finds the relevant page when you ask a question.
Generate: It sends that page + your question to the LLM to write the answer.
Step 1: The "Embedding" (Turning words into numbers)
Computers don't understand English; they understand math. To search through your documents, we need to convert your text into a list of numbers called a Vector.
Imagine a map.
"Dog" and "Puppy" are close together on the map.
"Dog" and "Sandwich" are far apart.
We use an Embedding Model (like OpenAI’s text-embedding-3-small or open-source alternatives) to turn your PDFs into these coordinates. We store these numbers in a Vector Database (like ChromaDB, Pinecone, or even Postgres with pgvector).
Step 2: The Retrieval (The Librarian)
Now, when a user asks: "What is our refund policy?"
We don't send that straight to the LLM. First, we convert that question into numbers (vectors) too.
Then, we search our database: Which document is mathematically closest to this question?
The database replies: "Hey, 'Refund_Policy_2025.pdf' is a 95% match."
Step 3: The Generation (The Magic Trick)
This is the part that feels like magic, but it’s just prompt engineering.
We take the document we found in Step 2, and we paste it into a prompt that looks like this:
"You are a helpful assistant. Answer the user's question using ONLY the context provided below.
Context: [Insert text from Refund_Policy_2025.pdf]
User Question: What is our refund policy?"
Now, the AI isn't hallucinating. It’s summarizing the text you just gave it.
Why build from scratch?
You might ask, "Frank, why not just use a tool that does this for me?"
Because when it breaks (and it will), you need to know where it broke.
Did it fail to find the document? (Bad Embeddings)
Did it find the document but fail to answer? (Bad LLM)
Is the data messy? (Bad Ingestion)
Building the basic pipeline yourself, even just a simple Python script, gives you the intuition to debug the complex systems later.
Final Thoughts
RAG isn't just a trend; it's the standard for how businesses will interact with AI. We are moving away from "Chat with a bot" to "Chat with your data."
If you are a developer in 2026, understanding this flow is as important as understanding how a database works.
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