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Illustration: RAG explained: how to make your own documents searchable with AI

By Ivo Donker — created with AI assistance (Claude & Gemini) · Last updated: July 31, 2026

RAG explained: how to make your own documents searchable with AI

A standard Large Language Model (LLM) is trained on massive amounts of public data, but it knows nothing about your specific, internal documents. How do you ensure that an AI can read and understand your own notes, reports, or manuals? The answer is RAG, or Retrieval-Augmented Generation.

What is RAG?

RAG is a technique where you first have an AI model search an external database containing your documents before it generates a response. Instead of relying entirely on the 'knowledge' built into the model, you literally feed the model the most relevant text fragments in the prompt. This reduces hallucinations and ensures that the AI answers factually and contextually correctly.

Embeddings and Vector Databases (Under the Hood)

To search smartly by meaning (instead of just exact keywords), we use embeddings. An embedding model converts sentences and paragraphs into a long sequence of numbers, or vectors. Texts with a similar meaning get number sequences that are mathematically close to each other in a virtual space.

You store these vectors in a vector database. When you ask a question, your question is also converted into such a vector. The database quickly searches for the stored vectors that are closest to it (vector search). This way, the system finds the right documents, even if you use slightly different words.

The Step-by-Step Plan

1
Preparing documents: Split your long PDFs or text documents into smaller, manageable pieces (chunks).
2
Creating embeddings: Send these pieces of text to an embedding model to convert them into vectors.
3
Saving: Store the original text and the corresponding vectors in a vector database.
4
Searching (Retrieval): Convert the end-user's search query into a vector and retrieve the most relevant text fragments.
5
Generating (Generation): Send the retrieved text along with the initial question to a powerful model, such as Claude Pro or DeepSeek. The model then formulates an accurate, readable answer based solely on your provided data.

Practical Exercise: Designing Your Own Knowledge Base

Imagine: you have a local folder full of PDF manuals for every appliance in your house — washing machine, dishwasher, router, thermostat, heat pump — plus warranty documents and your own troubleshooting notes from the past few years.

The assignment: Sketch on paper or in a text document what your RAG pipeline would look like to make all these manuals searchable. Which framework (such as LangGraph or LangChain) would you use to parse your documents? And how do you ensure that a question like "error code E4 on the dishwasher" directly retrieves the correct PDF section?

Want to know more about the underlying models you can use for generation after the retrieval step? Then check out our guide on LLM models and architectures.