RAG with Your Own Hands: How to Feed Your Documents and Knowledge Base to AI

Have you ever tried asking ChatGPT a question about your internal regulations or technical documentation? Most likely, the answer was generic, or even completely wrong. The reason is simple: large language models are trained on public data and don't know your unique documents. There is a solution — Retrieval Augmented Generation (RAG). Anyone can build their own RAG system, and today we'll break down how to do it without diving into the depths of machine learning. ## What is RAG and Why Do You Need It RAG is an architectural pattern that allows an AI model to answer questions based on your own data. Instead of relying solely on "built-in" knowledge, the model first finds relevant fragments in your database (via vector search) and then generates an answer based on them. This approach eliminates neural network hallucinations and makes answers accurate. For example, you can upload contracts, instructions, or company FAQs into the system, and the AI will advise clients strictly based on those documents. ## Step 1. Loading and Preparing Documents The first step is to gather all the files you want to "feed" to the AI. These can be PDFs, Word documents, TXT files, Markdown, or even HTML pages. It's important that the documents are in a readable text format (scanned PDFs require OCR). Next, the documents need to be split into small pieces — this is called **chunking**. The chunk size critically affects the quality of the answer. A chunk that is too small will lose context; one that is too large will make search difficult. | Chunk Size (tokens) | Pros | Cons | |---------------------|------|------| | 128–256 | High search precision | Loses contextual connection | | 512–1024 | Balance of precision and context | Requires overlap tuning | | 2048+ | Good context | Slow search, noise | I recommend starting with 512 tokens and a 10–15% overlap. This will give good quality without overloading the system. ## Step 2. Turning Text into Embeddings After chunking, each text fragment needs to be converted into a vector — a numerical representation of meaning. This process is called **embedding**. Special encoder models are used for this, such as `text-embedding-ada-002` from OpenAI or the open-source `all-MiniLM-L6-v2`. Embeddings allow measuring semantic similarity: texts with similar meanings end up close together in a multidimensional space. When a user asks a question, it is also turned into a vector, and the system searches for the nearest chunks from your database. ## Step 3. Vector Database Specialized databases — **vector databases** — help store embeddings and search through them quickly. The most popular ones are: - **Chroma** — lightweight, embeddable, great for prototypes. - **Pinecone** — cloud-based, scalable, but paid. - **FAISS** from Facebook — a library for local search, very fast. - **Qdrant** — open-source, with good performance. For a first RAG project, it's best to use Chroma. It installs via pip and doesn't require server setup. ## Step 4. Building the Generation Pipeline Once the vector database is ready, all that's left is to connect everything into a chain: 1. Get the user's question. 2. Convert it into an embedding using the same model. 3. Find the top 3–5 most similar chunks in the vector database. 4. Pass these chunks to the LLM (e.g., GPT-4 or Llama 3) along with the question. 5. Get an answer based on your documents. This entire pipeline can be implemented in Python in literally 50–100 lines of code. Modern frameworks like LangChain or LlamaIndex make it even easier — they provide ready-made tools for loading, chunking, and searching. ## Practical Tips for Successful RAG - **Data cleanliness:** Remove watermarks, headers, footers, and extra markup from documents — they add noise to embeddings. - **Metadata:** Add the document name, date, and section to each chunk — this improves filtering. - **Testing:** Test the system with real questions. If the answer is wrong, it's most likely

RAG with Your Own Hands: How to Feed Your Documents and Knowledge Base to AI

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